bluuebunny/crossref_metadata_2025_split huggingface dataset:
The morphological and mechanical characterization of UHMWPE based composite: an experimental study
Authors: Basrani Jitendra , Kumar Manoj , Kumar Pramod 
Published: May 2023

Abstract
Natural fiber and its hybrid composites have a promising future due to their economic, lightweight, environmentally superior, and sustainable properties. The purpose of this research is to explore the morphological and physical properties of composites made of hemp-epoxy and ultra-high molecular weight polyethylene-hemp-epoxy. The compression moulding technique is used to fabricate the UHMWPE -hemp hybrid composite and hemp-epoxy sample at a different orientation. These samples are subjected to different types of mechanical testing, including flexural, impact, hardness, and tensile tests at different strain rates as per ASTM standards. The result indicates that the flexural strength, impact strength, and hardness of the hybrid composite are more than the hemp composite. Scanning electron microscopy has been used to examine fractography at various strain rates. It is found that with the increase in the strain rate, tensile strength increases. It is observed that there is an increase of more than 56.49% in the specimen’s strength with the addition of 8.3% ultrahigh molecular weight polyethene fiber reinforcement.
Broadband achromatic electromagnetic beam control based on reflective metamaterial
Authors: Zhang Simeng , Li Chenxia 
Published: May 2023

Abstract
The emergence of metasurfaces has shown great potential in planar optics. From visible light to infrared wavelengths, various forms of metasurfaces have been studied to control electromagnetic waves. Due to the dispersive properties of light waves, the application of metasurfaces in broadband devices is limited. We design a metasurface for broadband efficient achromatic transmission in the terahertz band. After optimized design, an achromatic deflector and an achromatic focusing device were obtained. By changing the geometric parameters of the metal ring to meet the phase and dispersion requirements of the metasurface, broadband achromatic metasurfaces and focusing metasurfaces are realized in the range of 1.8–2.3 THz.
Salient Object Detection From Unlabeled Images
Authors: Li Sentan , He Xiaoye , Wang Liyang , Huang Xiaoming 
Published: May 2023

Abstract
Salient object detection via deep neural networks usually needs a large amount of images with human annotation. To avoid laborious and consuming annotation, we propose a robust unsupervised salient object detection method with three stages in this work. Our method first uses unlabeled data to generate an activation map which indicates the coarse location of object. Then one scribble generation method based on the activation map is proposed, which provides foreground scribble and background scribble with high confidence. Finally, a salient object detection model is trained with the supervision of the generated scribble. Performance comparison is carried on four public datasets, showing that our method significantly outperforms state-of-the-art unsupervised methods, and also achieves better performance than some weakly supervised methods.
A Review of Multi-Label Feature Selection
Authors: Wang Xiujuan , Zhou Yuchen 
Published: May 2023

Abstract
Feature selection has become a vital issue in data mining and machine learning. But some challenges have been outstanding when trying to improve the performance of feature selection, such as small sample, uncertain classes, complex features, complementation and redundancy between each feature. In this paper, firstly the background of feature selection is introduced. Then we have presented a new perspective to analyze multi-label feature selection and provided typical papers on different classifications. To further analyze these algorithms, evaluation criterion on results of multi-label feature selection is summarized. Finally, some reflects on research directions, future works and conclusions are organized.
UAV Swarm Cooperative Situation Awareness Theory Model Construction and Scenario Analysis
Authors: Gao Yang , Lv Na 
Published: May 2023

Abstract
The Unmanned Aerial Vehicle (UAV) swarm collaborative operations is one of the disruptive technologies in future information warfare. Considering the characteristics of UAV swarm collaborative operation, we propose two UAV swarm cooperative situation awareness (SA) theory models based on the classical ones and the composition of UAV swarm. Firstly, the improved Endsley three-level SA model is proposed, in which the traditional mental model is changed into human machine intelligent model on the individual level. Then the homogeneous swarm and heterogeneous swarm cooperative SA model are separately built based on the improved team SA model and Distributed SA (DSA) model. Meanwhile, a comparison between the multi-UAV and UAV swarm is carried. Finally, a scenario analysis of swarm cooperative SA in combat situation is presented.
THT: Transfer Head and Tail for Joint Entity Relation Extraction
Authors: Zou Huike , Jiang Jiawei , Fu Yan 
Published: May 2023

Abstract
Extracting entities and relations from unstructured text is the basic task of building knowledge graphs. Various methods based on sequence labeling have been proposed recently, but most of them use uniform token representations to identify entities. It is clear that the first and last words of the head and tail entities should have different representations. For this reason, we propose a method based on word interaction that provides different representations for the first and last words of entities. Specifically, initial token representation is obtained by BERT. Each token interacts with the next k tokens for the entity’s first token recognition, interacts with the previous k tokens for the entity’s last token recognition, and merges them to obtain the head entity. Then recognize the tail entities based on the head entities and form entity pairs. Finally, get the triples based on a simple multi-classification for relation determination. Extensive experimental results on four datasets show that THT performs better than state-of-the-art baselines.
AVC Planning Strategy of Power Grid Based on Genetic Algorithm Improved Neural Network Quantile Regression
Authors: Lv Chenxu 
Published: May 2023

Abstract
Aiming at the security risk of power system instability, this paper proposes a parameter optimization AVC planning strategy based on neural network. Based on a variety of power grid operation modes, the strategy innovatively uses genetic algorithm to improve the neural network quantile regression control variables to obtain state variables. This method can reduce the frequency of invalid operations, make the AVC control strategy more stable, and improve system security. In the actual case, the improved neural network quantile regression of genetic algorithm improves the accuracy by at least 29% compared with multiple linear regression.
AJE: Attention Mechanism for Entity-relation Joint Extraction
Authors: Gui Wei , Cui Aixiang 
Published: May 2023

Abstract
Joint extraction of entities and relations is an significant issue of information extraction, which is very helpful for many downstream tasks, including knowledge base construction, question answering, and biomedical text diagnosis[1], etc. The common approach of existing models is to extract the subject and the relation first, then compute the subject and the relation to obtain the object, and finally, the triplet is judged. However, such an approach cannot efficiently handle of information extraction, and the results are not very good for Subject-Object Overlap (SOO) case. In this paper, a joint entity-relation extraction method AJE is proposed based on dot-product attention mechanism. The method first maps subject, object and relationship into three matrices of Q, K and V. After that, the attention weighting is achieved on these three matrices and the output vector is used to determine whether the triple is correct or not. The F1-score is used in experiments to show that the proposed model is more efficient than the current existing ones. It also has better results in handling other cases such as SOO, multi-triple problem, etc.
Intelligent Temperature Control of Pressure Gauge Calibration Based on ANT-LION Neural Network
Authors: Yang Longyu , Yang Jiejie , Xie Yanhui , Chai Yi , Ma Le 
Published: May 2023

Abstract
Aiming at the actual industrial requirements of high accuracy and high efficiency in the industrial process of pressure gauge calibration, this paper studies the intelligent temperature control of the pressure gauge calibration system. Based on the physical mechanism of the calibration system, the controlled object model is established. According to the characteristics of large time delay and nonlinearity of the calibration industrial process, a neural network PID control method based on ANT-LION optimization algorithm is proposed. The experimental results show that the method is effective and reliable, which improves the speed and stability of the temperature control of the pressure gauge calibration system, and realizes the intelligent control of the temperature of the calibration system.
Meta-Learned User Preference Estimator with Attention Network for Cold-Start Recommendation
Authors: Liu Shilong , Liu Yang , Zhang Xiaotong , Xu Cheng , He Jie , Qi Yue 
Published: May 2023

Abstract
One crucial challenge in the recommendation research field is the cold-start problem. Meta-learning is a feasible algorithm to reduce the error of cold-start recommendation because it can adjust to new tasks rapidly through relatively few updates. However, meta-learning does not take the diverse interests of users into account, which limits the performance improvement in cold-start scenarios. We proposed a recommendation model called attentional meta-learned user preference estimator that combines attention network and meta-learning. This method enhances the ability of modelling the personalized user interest by learning the weights between users and items based on attention mechanism, then improves the performance of cold-start recommendation. We validated the model with two publicly available datasets in the recommendation field. Compared with the three benchmark methods, the proposed model reduces the mean absolute error by at least 2.3% and the root mean square error of 2.5%.
Deep metric learning based approach for network intrusion detection
Authors: Fu Xingbing , Zhang Xuewen , Fu Jianfeng , Wu Bingjin , Zhang Jianwu 
Published: May 2023

Abstract
Today, intrusion detection systems are the way to defend network intrusion flows. In this paper, to categorize network traffic data, we proposed a novel method for detecting network intrusions. It builds intrusion detection models using a deep metric learning (DML) strategy that incorporates two multi-scale convolutional neural networks (MSCNN) and a Triplet network. During the phase of training MSCNN networks, the network traffic data are divided into attack network traffic data and normal data, and we train two distinct MSCNN networks on the basis of these two datasets. To determine the distance between the network traffic data, a Triplet network is trained to learn a mapping space which preserves the relationships of attacks and normal network flows. Soft-margin triplet loss is used as a loss function to train the Triplet network. In the prediction stage, each new flow passes through two MSCNN networks, which reconstruct the network flows, and then the Triplet network measures the distance between the network flow and the reconstructed flow. To verify the model’s advantages in intrusion detection, we compare the performance of the MSCNN+Triplet network, the CNN+LSTM, and the shallow neural network. Experimental results reveal the proposed scheme has superior detection performance compared to other intrusion detection schemes. The code will be available at https://gitee.com/wbsk/mscnn_triplet/tree/master/.
Health Assessment of Flow Meter Calibration Device Based on Fuzzy Comprehensive Evaluation
Authors: Yang Jiejie , Yang Longyu , Chai Yi , Ma Le , Liu Qie 
Published: May 2023

Abstract
In view of the rapid development of related technologies in the field of computer and artificial intelligence at home and abroad, the health maintenance mode of the existing flowmeter calibration device is still very lagging behind. The actual composition structure and principle of the device are studied to determine the multi-level evaluation index of the device. Using the actual operation data of the calibration pipeline, the parameter feature vector is extracted as a healthy sample. By calculating the combined weight and fuzzy membership matrix of evaluation indexes at all levels, the membership matrix of health status is solved, and the health status of equipment is classified and evaluated according to fuzzy logic. The example analysis shows that the method can reflect the real health status of the equipment to a certain extent and improve the timeliness of the health assessment of the calibration device.
Hybrid Encoding Evolutionary Algorithm for Reversible Logic Synthesis
Authors: Wang Xiaoxiao , Wang Ziheng , Su Shengqiang , Wang Jinghao , Ji Yingchao 
Published: May 2023

Abstract
Variable-length encoding evolutionary algorithm has been proved effective in small and medium-sized reversible logic synthesis. Variable-length chromosome is adopted because the length of optimal reversible circuit is unknown. However, it leads to huge search space. The improved two-stage hybrid encoding synthesis is proposed to reduce the search space and enhance the feasible ratio. Fixed and variable-length encoding are applied in the two stages respectively. Estimated maximum length is obtained through heuristic information from the positive polarity Reed Muller spectra and the fixed chromosome length in the first stage is set to a value less than the maximum one. When the population converges in the first stage and the feasible solution has not found, the second stage starts to work. It employs the original variable-length encoding algorithm, but the optimization problem is simplified through the reduced positive polarity Reed Muller spectra of the best solution from the first stage. Experimental results show that both the fixed-length encoding and the simplified fitness function in the two stages both can decrease the search space and save the synthesis time.
Data Fusion Algorithm For Wireless Sensor Networks Based On Cluster Routing And Deep Learning
Authors: Hu Ting 
Published: May 2023

Abstract
In order to make full use of node energy and extend the network’s lifetime, a data fusion algorithm for wireless sensor networks is proposed in this paper. The algorithm improves the traditional clustering routing protocol and introduces a deep learning model to build the data fusion algorithm based on the optimized clustering architecture. When optimizing cluster routing protocol, the cluster head is selected by comprehensively considering such factors as node density, residual energy of nodes and the reasonable cluster size. The deep learning model and classification model are introduced in the data fusion process. The optimal parameters of the network are obtained by training the deep learning model of the sink node, then the sink nodes transfer these parameters to the corresponding sensor nodes. The cluster head extracts, classifies and fuses similar features of the data. The nodes in the cluster use the deep learning model to fit the collected raw data. Simulation results show that the data fusion algorithm proposed in this paper can reduce the redundant data transmitted to the sink node of wireless sensor networks, reduce the network energy consumption and prolong the network’s life. The overall performance of the network is improved.
A novel Tunnel Positioning Approach via Long Term Evolution Cellular Signal
Authors: Jia Huiqiang , Jia Kebin , Tian Xiuchen , Xiong Daoquan 
Published: May 2023

Abstract
Currently, Radio Frequency Identification System (RFIS), ZigBee and Ultra-Wide Band (UWB) methods are mainly used to positioning in enclosed space. But they require complex hardware layout and high hardware costs, resulting in the inability to meet the positioning needs of complex environments. Therefore, we designed a novel tunnel positioning approach via Long Term Evolution (LTE) cellular signal. This approach includes: signal acquisition, data preprocessing, feature database construction, model training and real-time positioning. In the data preprocessing stage, we adopt 3sigma and Kalman filtering to filter outliers and noise, and use information gain and information gain rate to select effective features. In the real-time positioning phase, a combination of K-Weighted Nearest Neighbor (KWNN) and Support Vector Regression (SVR) is used for positioning in the tunnel. To verify the designed approach, we did an experiment using data from the actual tunnel. The experimental results show that this approach has better positioning accuracy than FK-NN [1] and TSVR [2].
Exploring hand gesture recognition using micro-Doppler radar data based on vision transformers
Authors: Qu Chongxiao , Zhang Yongjin , Jin Lei , Fan Changjun , Liu Shuo , Chen Xiayan 
Published: May 2023

Abstract
Nowadays, radar technology is undergoing rapid development. Contrary to the old stereotype of radar systems being large, complex and mainly used for detecting targets at a long distance, small and compact radio frequency products are getting flourishing and widely applied. Benefiting from this, hand gesture recognition using a low-cost, low-power consuming radar is becoming an area of wide interest and study. In this paper, we explore using low-cost FMCW (Frequency Modulated Continuous Wave) radar modules for gesture recognition based on vision transformers. First, we pre-process the radar data and convert the 1-dimensional spectrogram complex samples into 2-dimensional matrices just like images. Then, we adopt an existing model based on vision transformers to classify them, which leverages existing state-of-the-art technologies for vision recognition problems to address our issue. Experimental results show that our scheme yields good performance and it is a promising method.
Natural Gas Load forecasting based on the fusion of VMD-SE and CNN-BiLSTM-Attention
Authors: Bian Genqing , Yang Yang , Guo Xusen , Qu Wenjing , Xi Wenjing 
Published: May 2023

Abstract
The rational dispatch and steady supply of natural gas resources can be effectively supported by accurate forecasting of the natural gas load. The accuracy of natural gas load forecasting is, however, low due to the fundamental characteristics of the non-stationary and non-linear natural gas load. Based on this, this paper proposes a new natural gas load forecasting model based on the fusion of VMD-SE and CNN-BiLSTM-Attention. First, the average instantaneous frequency method is used to determine the optimal K value of VMD, and then the sample entropy method is introduced to reconstruct the sub-sequence of VMD decomposition, which effectively reduces the computational overhead of the algorithm. Then, using CNN to mine the level information of multi-dimensional time series variables, using BiLSTM to mine the time series feature information, and introducing the Attention mechanism to give corresponding weight to different feature factors, highlighting the influence of key factors, the anticipated values of each reconstructed sub-series are superimposed to provide the final predicted value of the natural gas load. The model proposed in this paper has lower error than other forecasting models, can effectively improve the precision of natural gas load forecasting, and has a better forecasting effect, according to the results of establishing various forecasting models using the natural gas load data of one region of China as an example.
A Novel Disease Prediction Model Based on Blockchain and Federated Learning
Authors: Wang Bowen , Cheng Ziwen , Yi Liu , Cheng Zhu 
Published: May 2023

Abstract
With the advent of the era of big data, the model architecture of disease prediction has been transformed from cloud computing architecture to edge computing architecture to alleviate problems, such as single point of failure, huge burden of computing and long communication delays.However, due to the lack of trust among edge computing participants (herein referred to as medical institutions with data resources or data storage nodes), the patients’ personal privacy and security issues are still challenging, and there is an urgent need to propose new solutions with higher security. In this paper, we proposed a new disease prediction model with the edge computing framework based on blockchain and federated learning technology to realise full mining of data value, as well as protecting local data sharing security and privacy. In the process of federated learning with blockchain as the underlying platform working environment, we propose the concept of model accuracy as the selection indicator of data nodes in federated learning, which improves the accuracy of the federated learning model and optimizes the training delay.
Research and Design of Real Time Big Data Visualization Analysis Platform Based on Flink
Authors: Zhang Hang 
Published: May 2023

Abstract
Real-time big data analysis provides a better value than standard offline big data analysis. Academic groups are still researching and exploring the technologies of real-time big data analysis. Due to the vast scale and poor transmission rate of big data, there are several impediments in the research process, making it hard to fully access the potential value of data.A real-time large data visualization analysis platform based on Flink is presented to overcome this challenge.The platform first collects data in real time and sends it to the message middleware Kafka, and then the big data analysis platform reads the data stream in Kafka in real time and transmits it to the multidimensional operator developed with Flink as the framework for real-time Calculate, and uses WebSocket technology to push the calculation results to the front end in real time, and perform visual display, enabling real-time monitoring and analysis of data flow.The experimental findings suggest that the real-time big data analysis platform based on the integration of Flink and Kafka can accurately, promptly, and clearly portray the real-time data flow process and visual analysis.
Sentiment Analysis of Social Network Text Based on HDBSCAN and SO-PMI
Authors: Yang Yuxuan , Wei Yingmei , Gao Min , Ran Zanxi , Wang Qi 
Published: May 2023

Abstract
With the rapid development of the Internet, the length and themes of text content in social network environments are becoming more and more abundant. The probability and degree of semantic similarity between different words in different texts are also increasing. It is difficult to effectively identify text topics and their sentiment tendencies. This paper takes the text data of the Ford Forum of the PCauto as the research object, builds a word vector model based on Word2vec, uses HDBSCAN to classify social network context, and calculates the sentiment value of different topic through SO-PMI. It aims to explore the complexity of the semantic evolution and feature extraction process and its generating text topics, based on which we further analyze the variability of sentiment tendencies between words and topics and then summarize their characteristics.