bluuebunny/crossref_metadata_2025_split huggingface dataset:
Construction of SDN Network Management Model Based on Virtual Technology Application
Authors: Shu Zhong , Deng Boer , Tian Luo , Duan Fen , Sun Xinyu , Chen Liangzhe , Luo Yue 
Published: July 2022

AbstractThis paper designs a virtual SDN network management model constrained by fair and equal network management information access mechanisms by analyzing the problems existing in the universality of existing SDN network management models. Starting with the three-tier structure of the SDN network management system, the main parameters involved in the network management service function, information processing and transmission channel construction in the system were strictly and normatively defined. The design of virtual nodes is regarded as the core element of the network management system, and the information transmission inside it adopts logical operation; The network management service function and the channel for realizing the network management service function are isolated, and the iterative search, analysis and update mechanism is enabled in the network management information transmission channel. By constructing the experimental verification platform and setting the evaluation parameters of the system performance objectives, the scalability and timeliness of the model were evaluated from two aspects: the deployment of network virtual nodes and the dynamic control of network management information channels. The collected experimental core evaluation parameters, the realization time of the network management service function, can show that the dynamic distribution mechanism of network management information can be cross-applied to each virtual node, and the channel update mechanism of network management information can adjust the information processing queue in real-time. The network management system model that has been built realizes the separation of management and control of the network management system and has the characteristics of independent operation, autonomous function, self-matching, rapid deployment and dynamic expansion.
External Information Security Resource Allocation with the Non-cooperation of Multiple Cities
Authors: Li Jun , Cheng Dongsheng , Xing Lining , Tan Xu 
Published: July 2022

AbstractThe external information security resource allocation method is proposed considering the non-cooperation of multiple cities. In this method, the effects of different influence factors, for example, city size, probability of intrusion by illegal users and propagation probability of one-time intrusion on resource allocation is explored. Through the simulation experiment, the proposed conclusions are conveniently and clearly verified.
FSTOR: A Distributed Storage System that Supports Chinese Software and Hardware
Authors: Lin Yuheng , Wang Zhiqiang , Zhao Jinyang , Chen Ying , Chi Yaping 
Published: July 2022

AbstractIn order to develop a distributed storage system that adapts to Chinese software and hardware, build a cloud computing platform that is independently usable, safe and reliable, data utilization is more concentrated and intelligent, and service integration is more unified and efficient. This paper designed and implemented a distributed storage system that supports Chinese software and hardware, which is compatible with Chinese mainstream CPU, operating system, database, middleware and other software and hardware environments. After a lot of experiments and tests, it is confirmed that the system has high availability and high reliability.
Joint Calibration Based on Information Fusion of Lidar and Monocular Camera
Authors: Zheng Li , Peng Haolun , Liu Yi 
Published: July 2022

AbstractTo solve the problem of joint calibration in multi-sensor information fusion, a joint calibration technique based on three-dimensional lidar point cloud data and two-dimensional gray image data is proposed. Firstly, by extracting the corner information of the gray image data, the two-dimensional coordinates of the corner were obtained, and the calibration of the monocular camera was completed by using the corner information, and its internal and external parameters were obtained. Then, by extracting the corner information of the point cloud data obtained by lidar, the corresponding corner points are matched. Finally, the rotation and translation matrix from lidar coordinate system to image coordinate system is generated to realize the joint calibration of lidar and camera.
PCCP: A Private Container Cloud Platform Supporting Domestic Hardware and Software
Authors: Wang Zhuoyue , Wang Zhiqiang , Zhao Jinyang , Chi Yaping 
Published: July 2022

AbstractWith the widespread use of container cloud, the security issue is becoming more and more critical. While dealing with common security threats in cloud platforms and traditional data centres, there are some new security issues and challenges in the container cloud platform. For example, there are significant challenges in network isolation and resource management. This paper proposes a private container cloud platform PCCP based on Docker supporting domestic software and hardware to solve these security problems. This paper introduces the system architecture and functional architecture of the platform. The system has been tested and confirmed to have high availability and high reliability. The platform gives full play to the value of domestic software and hardware and is better able to serve the information construction of our country.
From Data Literacy to Co-design Environmental Monitoring Innovations and Civic Action
Authors: Happonen Ari , Wolff Annika , Palacin Victoria 
Published: July 2022

AbstractSENSEI is an environmental monitoring initiative run by Lappeenranta University of Technology (LUT University) and the municipality of Lappeenranta in south-east Finland. The aim was to collaboratively innovate and co-design, develop and deploy civic technologies with local civics to monitor positive and negative issues. These are planned to improve local’s participation to social governance issues in hand. These issues can be e.g. waste related matters like illegal dumping of waste, small vandalism into city properties, alien plant species, but on the other hand nice places to visits too. This publication presents initiatives data literacy facet overview, which is aimed at creating equitable access to information from open data, which in turn is hoped for to increase participants motivation and entrepreneurship like attitude to work with the municipals and the system. This is done by curating environmental datasets to allow participatory sensemaking via exploration, games and reflection, allowing citizens to combine their collective knowledge about the town with the often-complex data. The ultimate aim of this data literacy process is to enhance collective civic actions for the good of the environment, to reduce the resource burden in the municipality level and help citizens to be part of sustainability and environmental monitoring innovation activities. For further research, we suggest follow up studies to consider on similar activities e.g. in specific age groups and to do comparisons on working with different stage holders to pin point most appropriate methods for any specific focus group towards collaborative innovation and co-design of civic technologies deployment.
Information Security Resource Allocation Using Evolutionary Game
Authors: Li Jun , Cheng Dongsheng , Xing Lining , Tan Xu 
Published: July 2022

AbstractBased on the discussion of related concepts and technical theories, the information security resource allocation influencing factors index system is constructed from four aspects: resources, threat sources, vulnerabilities and security measures. With the further analysis of information security factors and their affecting mechanisms, the basic theoretical framework of information security resource allocation is established based on the evolutionary game. Under this framework, the subject relationship in various situations is analyzed. This research work can conduct a reasonable allocation of resources related to information security.
Design of Multi-channel Pressure Data Acquisition System Based on Resonant Pressure Sensor for FADS
Authors: Fan Xianguang , Mao Hailing , Zhu Chengxiang , Wu Juntao , Xu Yingjie , Wang Xin 
Published: July 2022

AbstractResonant pressure sensors have high accuracy and are widely used in meteorological data acquisition, aerospace and other fields. The design and experiment of multi-channel pressure data acquisition system based on resonant pressure sensor, which used for the flush air data sensing(FADS) system, are described. The hardware architecture of DSP and FPGA is applied to the data acquisition system. The digital cymometer and 16-bit analog-to-digital converter are used to measure the output signal of the sensor. It is shown the data acquisition system has favourable performance within the operating temperature range. The maximum experimental error is less than 0.02%FS over the range 2–350 kPa. The period of sampling and fitting is less than 8 ms. The frequency and voltage measurements meet accuracy requirements. The calculated pressure and standard pressure result appears excellent linearity, which reach up to 0.9999.
Research on the Design of Community Residential Space from the Perspective of Digitization
Authors: Gao Chenglin , Tong Shuo 
Published: July 2022

AbstractThe residential architecture in the process of urban digital development has become a living complex with real and virtual mirrors, in which people are the unity of connection between spatial environment, identity and living relationship. In this paper, the new value orientation of community residential design is analyzed by sorting out the meaning of community; within the design system of residential space, the intimacy and public consciousness of residents’ neighborhood relationship is enhanced through spatial transition and cultivation of shared living space. The argument is developed from three levels: individual residents’ self-reconstruction, residents’ new behavioral decisions, and spatial behavioral output. Through a series of argumentation, the relationship between community and residential space planning and design is explored, and the data on the interaction between users, usage behavior and space usage of different households are statistically obtained. At the same time, this paper simulates and designs the community residential space module system based on this data and combined with the computer 3D model derivation. The residential block formed by the combination of the smallest modules, as the smallest residential unit, continues to form the design path of a sustainable residential system through the process of combination and deformation of space.
A User-Interaction Parallel Networks Structure for Cold-Start Recommendation
Authors: Lin Yi 
Published: July 2022

AbstractThe goal of the recommendation system is to recommend products to users who may like it. The collaborative filtering recommendation algorithm commonly used in recommendation systems needs to collect explicit/implicit feedback data, and new users do not leave behavioral data on the product, which leads to cold-start problem. This paper proposes a parallel network structure based on user interaction, which extracts features from user interaction information, social media information, and comment information and forms a matrix. The graph neural network is introduced to extract high-level embedded correlation features and the role of parallelism is to reduce computing cost further. Experiments based on standard data sets prove that this method has a certain degree of improvement in NDCG and HR indicators compared to the baseline.
Orchard Energy Management to Improve Fruit Quality Based on the Internet of Things
Authors: Zhang Pingchuan , Wang Sijie , Li Xiaowen , Chen Zhao , Chen Xu , Hu Yanjun , Zhang Hangsen , Zhang Jianming , Li Mingjing , Huang Zhenzhen , Li Yan , Li Liutong , Xu Xiaoman , Yang Yiwen , Song Huaping , Huo Huanhuan , Shi Yiran , Hu Xueqian , Wu Yabin , Wang Chenguang , Chen Feilong , Yang Bo , Zhang Bo , Zhang Yusen 
Published: July 2022

AbstractThe crop growth is an energy conversion process, and energy management has an important impact on the quality and yield of crop products. As IoT (the Internet of Things) is widely used in agriculture, for example, orchard IoT is often used to realize water-saving irrigation, this paper innovatively proposes a scheme to improve fruit quality by using IoT to realize orchard energy management. The designed Internet of things, in addition to the usual orchard environmental parameters and water-saving irrigation, can further adjust the temperature difference between day and night according to the local temperature, that is, by spraying low-temperature water mist at 16 ℃ to reduce the ambient temperature of the orchard at night, creating an environment conducive to the conversion of carbohydrate into sugar. The experiment in peach orchard shows that the orchard energy management method based on Internet of Things works effectively, which can reduce the peach orchard temperature to 20° at night in summer, which is beneficial to improve the peach fruit sweetness.
Development of Deep Learning Algorithms, Frameworks and Hardwares
Authors: Ji Jinbao , Hu Zongxiang , Zhang Weiqi , Yang Sen 
Published: July 2022

AbstractAs the core algorithm of artificial intelligence, deep learning has brought new breakthroughs and opportunities to all walks of life. This paper summarizes the principles of deep learning algorithms such as Autoencoder (AE), Boltzmann Machine (BM), Deep Belief Network (DBM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Recursive Neural Network (RNN). The characteristics and differences of deep learning frameworks such as Tensorflow, Caffe, Theano and PyTorch are compared and analyzed. Finally, the application and performance of hardware platforms such as CPU and GPU in deep learning acceleration are introduced. In this paper, the development and application of deep learning algorithm, framework and hardware technology can provide reference and basis for the selection of deep learning technology.
A Fully-Nested Encoder-Decoder Framework for Anomaly Detection
Authors: Gong Yansheng , Jing Wenfeng 
Published: July 2022

AbstractAnomaly detection is an important branch of computer vision. At present, a variety of deep learning models are applied to anomaly detection. However, the lack of abnormal samples makes supervised learning difficult to implement. In this paper, we mainly study abnormal detection tasks based on unsupervised learning and propose a Fully-Nested Encoder-decoder Framework. The main part of the proposed generating model consists of a generator and a discriminator, which are adversarially trained based on normal data samples. In order to improve the image reconstruction capability of the generator, we design a Fully-Nested Residual Encoder-decoder Network, which is used to encode and decode the images. In addition, we add residual structure into both encoder and decoder, which reduces the risk of overfitting and enhances the feature expression ability. In the test phase, a distance measurement model is used to determine whether the test sample is abnormal. The experimental results on the CIFAR-10 dataset demonstrate the excellent performance of our method. Compared with the existing models, our method achieves the state-of-the-art result.
The Method for Micro Expression Recognition Based on Improved Light-Weight CNN
Authors: Luo Li , He Jianjun , Cai Huapeng 
Published: July 2022

AbstractIn view of the particularity of micro expression, there are some problems, such as resource waste or parameter redundancy in micro expression training and recognition by using large convolutional neural network model alone. Therefore, a method of using lightweight model to recognize micro expression is proposed, which aims to reduce the size of model space and the number of parameters, and improve the accuracy at the same time. This method uses mini-Xception as the framework and Non-Local Net and SeNet as parallel auxiliary feature extractors to enhance feature extraction. Finally, the simulation experiments are carried out on the two public data sets of fer2013 and CK+. After a certain training cycle, the accuracy can reach 74.5% and 97.8% respectively, which slightly exceeds the commonly used classical models. It is proved that the improved lightweight model has higher accuracy, lower parameters and model size than the large convolution network model.
A Lightweight Verification Scheme Based on Dynamic Convolution
Authors: Tang Lihe , Yang Weidong , Gao Qiang , Xu Rui , Ye Rongzhi 
Published: July 2022

AbstractSince Electricity Grid Engineering involves a large number of personnel in the construction process, face recognition algorithms can be used to solve the personnel management problem. The recognition devices used in Electricity Grid Engineering are often mobile, embedded, and other lightweight devices with limited hardware performance. Although a large number of existing face recognition algorithms based on deep convolutional neural networks have high recognition accuracy, they are difficult to run in mobile devices or offline environments due to high computational complexity. In order to maintain the accuracy of face recognition while reducing the complexity of face recognition networks, a lightweight face recognition network based on Dynamic Convolution is proposed. Based on MobileNetV2, this paper introduces the Dynamic Convolution operation. It proposes a Dynamic Inverted Residuals Block, which enables the lightweight neural network to combine the feature extraction and learning ability of large neural networks to improve the recognition accuracy of the model. The experiments prove that the proposed model maintains high recognition accuracy while ensuring lightweight.
Cross-Knowledge Graph Entity Alignment via Neural Tensor Network
Authors: Wang Jingchu , Liu Jianyi , Chen Feiyu , Lu Teng , Huang Hua , Zhao Jinmeng 
Published: July 2022

AbstractWith the expansion of the current knowledge graph scale and the increase of the number of entities, a large number of knowledge graphs express the same entity in different ways, so the importance of knowledge graph fusion is increasingly manifested. Traditional entity alignment algorithms have limited application scope and low efficiency. This paper proposes an entity alignment method based on neural tensor network (NtnEA), which can obtain the inherent semantic information of text without being restricted by linguistic features and structural information, and without relying on string information. In the three cross-lingual language data sets DBPFR−EN, DBPZH−EN and DBPJP−EN of the DBP15K data set, Mean Reciprocal Rank and Hits@k are used as the alignment effect evaluation indicators for entity alignment tasks. Compared with the existing entity alignment methods of MTransE, IPTransE, AlignE and AVR-GCN, the Hit@10 values of the NtnEA method are 85.67, 79.20, and 78.93, and the MRR is 0.558, 0.511, and 0.499, which are better than traditional methods and improved 10.7% on average.
A Method for Obtaining Highly Robust Memristor Based Binarized Convolutional Neural Network
Authors: Huang Lixing , Diao Jietao , Teng Shuhua , Li Zhiwei , Wang Wei , Liu Sen , Li Minghou , Liu Haijun 
Published: July 2022

AbstractRecently, memristor based binarized convolutional neural network has been widely investigated owing to its strong processing capability, low power consumption and high computing efficiency.However, it has not been widely applied in the field of embedded neuromorphic computing for manufacturing technology of the memristor being not mature. With respect to this, we propose a method for obtaining highly robust memristor based binarized convolutional neural network. To demonstrate the performance of the method, a convolutional neural network architecture with two layers is used for simulation, and the simulation results show that binarized convolutional neural network can still achieve more than 96.75% recognition rate on MNIST dataset under the condition of 80% yield of the memristor array, and the recognition rate is 94.53% when the variation of memristance is 26%, and it is 94.66% when the variation of the neuron output is 0.8.
Real-Time Estimation of GPS Satellite Clock Errors and Its Precise Point Positioning Performance
Authors: Zou Junping , Wang Jiexian 
Published: July 2022

AbstractThe current stochastic model in GNSS processing is constructed based on the prior experience, for example the ratio of the weight of the pseudorange and phase observations is generally determined as 1:10000. These methods ignore the precision differences of the different GNSS receivers and observation space. In this paper, the standard deviation of differenced ionosphere-free pseudorange and phase observations is computed with dual-frequency observations and then the weight ratio of the pseudorange and phase observations is obtained using the computed standard deviation. This method is introduced in satellite clock estimating and the data is processed. The results show that the presented method is feasible, with which the accuracy of the estimated satellite clock results is improved. The estimated satellite clock results are further adopted in PPP and the positioning results of the 10 users validate that the estimated satellite clock, which uses the presented method, can accelerate the convergence of PPP compared with the traditional method.
Optimal Decision Threshold-Moving Strategy for Skewed Gaussian Naive Bayes Classifier
Authors: He Qinyuan , Yu Hualong 
Published: July 2022

AbstractGaussian Naive Bayes (GNB) is a popular supervised learning algorithm to address various classification issues. GNB has strong theoretical basis, however, its performance tends to be hurt by skewed data distribution. In this study, we present an optimal decision threshold-moving strategy for helping GNB to adapt imbalanced classification data. Specifically, a PSO-based optimal procedure is conducted to tune the posterior probabilities produced by GNB, further repairing the bias on classification boundary. The proposed GNB-ODTM algorithm presents excellent adaptation to skewed data distribution. Experimental results on eight class imbalance data sets also indicate the effectiveness and superiority of the proposed algorithm.
Open-Set Recognition of Shortwave Signal Based on Dual-Input Regression Neural Network
Authors: Zhang Jian , Wu Di , Hu Tao , Wang Shu , Wang Shiju , Li Tingli 
Published: July 2022

AbstractOpen-set recognition in blind shortwave signal processing is an important issue in modern communication signal processing. This paper presents a novel method for this problem. By preprocessing, the signal data matrix and vector diagram are obtained as network input. Then, the network is trained and tested with the known signal, and the upper and lower quintile algorithm is used to obtain the interval threshold for judging the known signal and the distance threshold for intercepting the length range of the unknown signal. Finally, the network is used for numerical regression in open-set range, the threshold combined with kernel density clustering algorithm is used to identify different signals. Simulation results show that the proposed method overcomes the defects of traditional algorithm, which cannot distinguish different types of unknown signals and only applicable for few signal types.