bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and Implementation of Intelligent Speech Recognition System Based on FPGA
Authors: Hu Huaixiang , Li Jiatong , Wu Chunchun , Li Xueyang , Chen Yuping 
Published: January 2022

Abstract
Under certain circumstances, where GPU is not available, the computing power of the CPU alone may not be sufficient enough to run speech recognition system efficiently. As a reconfigurable heterogeneous device, FPGA has the characteristics of high parallelism, low power consumption, and high flexibility. It can support high-performance computing acceleration for speech recognition applications. In order to implement speech recognition system on an specified computing platform, a speech recognition system architecture is proposed, which is based on software and hardware cooperative computing technology that focus on FPGA. First, a system architecture based on gated convolutional neural network is designed, with the simplification of the traditional speech recognition system architecture. Then a modular design idea is used to map the gated convolutional neural network operator to the FPGA hardware logic. Further through the state machine to schedule and control the data stream and operators, to achieve the flexible acceleration of the voice recognition network model. AISHELL data set is used to carry out the test, where presents remarkable improvement on computing performance, compared with Feiteng 1500A processor, the efficiency is improved by 18.1 times.
Research on 3D Defect Reconstruction in Plate with Phased Array Ultrasonic Test (PAUT)
Authors: Zheng Gangfeng , Wu Bolin , Li Ze , Liu Songfeng , Dong Hao , Zhang Haotian 
Published: January 2022

Abstract
In order to clarify the distribution and shape information of the defects inside the workpiece, it has become a new industrial testing requirement to display the defects more intuitively in three dimensions(3D). In this paper, by introducing 3D volume rendering method, the expansion of PAUT from 2D to 3D is realized. The method is used to reconstruct specific “gu-shaped [[CHECK_DOUBLEQUOT_ENT]] (“Gu” is the Pinyin in Chinese characters, and at the same time, “Gu” is the shape of the defect in the plate discussed in the paper.) defects, and the results of reconstruction are compared with actual defects. By using ultrasonic phased array focusing to detect defect C-scan images of different depths, the collected C-scan images were preprocessed and reconstructed according to the defect contour by volume rendering. The results show that the defect morphology with different depths can be well obtained by focused detection, and the reconstruction effect is good, which is consistent with the actual defect.
Research of Text Classification Based on TF-IDF and CNN-LSTM
Authors: Zhou Hai 
Published: January 2022

Abstract
With the rapid development of deep learning, many deep learning models have been widely used in Natural Language Processing(NLP). The Long-Short Term memory network(LSTM) model and convolutional neural network(CNN) model can achieve high classification accuracy in text classification tasks. However, the high input dimension of text features and the need to train a large number of parameters in the deep learning model often take a lot of time. This paper uses Term Frequency-inverse Document Frequency(TF-IDF) to remove features with lower weights, extract key features in the text, extract the corresponding word vector through the Word2Vec model, and then input it into the CNN-LSTM model. We compared the model with CNN, LSTM, and LSTM-attention methods and found that the model can significantly reduce model parameters and training time in short and long text data sets. The model hardly loses accuracy in the long text, but the model will lose a certain amount of accuracy in short texts. This paper also proposes fusing original text features to make up for the accuracy loss caused by the TF-IDF feature extraction method.
Large-scale Text Multiclass Classification Using Spark ML Packages
Authors: Wang Shuang , Luo Jian , Luo Liangfu 
Published: January 2022

Abstract
Machine learning is widely used in the field of natural language processing. Text classification is an important branch of natural language processing, mainly utilized to determine the topic of news information and judge the emotional tendency reflected by social data. However, with the expansion of data scale, traditional machine learning algorithms will encounter various performance bottlenecks due to the limitation of single node resource. The machine learning library ML provided by the distributed memory computing framework spark can efficiently process large-scale datasets. Based on Spark ML, this paper uses logistic regression to classify the preprocessed data originated from standard dataset 20newsgroups. The classification accuracy and F1-score can reach 89.64% and 89.64% respectively. Meanwhile, 5-fold cross validation experiments prove that the result is reliable and procedure design is feasible.
Incorporating Siamese Network Structure into Graph Neural Network
Authors: Zhang Yinan , Chen Wenyu 
Published: January 2022

AbstractSiamese network plays an important role in many artificial intelligence domains, but there requires more exploration of applying Siamese structure to graph neural network. This paper proposes a novel framework that incorporates Siamese network structure into Graph Neural Network (Siam-GNN). We use DropEdge as graph augmentation technique to generate new graphs. Besides, the strategy of constructing Siamese network’s paired inputs is also studied in our work. Notably, stopping gradient backpropagation one side in Siam-GNN is an important factor affecting the performance of model. We equip some graph neural networks with Siamese structure and evaluate these Siam-GNNs on several standard semi-supervised node classification datasets and achieve surprising improvement on almost every original graph neural network.
UniformLIME: A Uniformly Perturbed Local Interpretable Model-Agnostic Explanations Approach for Aerodynamics
Authors: Jiang Enshuo 
Published: January 2022

AbstractMachine learning and deep learning are widely used in the field of aerodynamics. But most models are often seen as black boxes due to lack of interpretability. Local Interpretable Model-agnostic Explanations (LIME) is a popular method that uses a local surrogate model to explain a single instance of machine learning. Its main disadvantages are the instability of the explanations and low local fidelity. In this paper, we propose an original modification to LIME by employing a new perturbed sample generation method for aerodynamic tabular data in regression model, which makes the differences between perturbed samples and the input instance vary in a larger range. We make several comparisons with three subtasks and show that our proposed method results in better metrics.
An Efficient Evaluation Method for Automobile Shells Design Based on Semi-supervised Machine Learning Strategy
Authors: Chen Wenxing , Dai Shuyang , Zheng Baojuan , Lin Hao 
Published: January 2022

Abstract
Automobile is one of the important modes of transportation for human travel in today’s society. Batch production in various countries in the world has also promoted the transformation of production concepts. At present, the development of the automobile industry is developing towards the trend of intelligence, personalizat-ion and sharing. Car appearance in a variety of ways, not every design is reasonable. Therefore, the main purpose of this article is to establish a scientific evaluation standard in order to large-scale test the quality of a variety of car shells design. The scientific nature is mainly reflected in combination the fluid-solid coupling knowledge and machine learning in this article, which can analyze the force of different shells in the flow field, and put out the cloud map information such as the stress, pressure and velocity of the shell. At last, analyze the best test samples and store them in the database, and then using semi-supervised heuristic algorithm to perform the sample training, the ultimate goal is to make the evaluation system more robust. The trained model can correctly evaluate each personalized car shape and give a reasonable score, which is convenient for car manufacturers to make best decision with personalized demand and scientific production.
Improving Word Alignment by Adding Gromov-Wasserstein into Attention Neural Network
Authors: Huang Yan , Zhang Tianyuan , Zhu Huidong 
Published: January 2022

Abstract
Statistical machine translation systems usually break the translation task into two or more subtasks and an important one is finding word alignments over a parallel sentence bilingual corpus. We address the problem of introducing word alignment for language pairs by developing a novel neural network model that can applied to other generative alignment models. We use Multi-layer attention model and multi-layer model with multi-head-attention mechanism on each layer provides superior translation quality. It can be trained on bilingual data without relying on word alignment. In this paper, we cast the correspondence problem directly as an optimal distance problem. We use the Gromov-Wasserstein distance to calculated how similarities between word pairs are related across languages. The resulting alignments dramatically outperform the GIZA++ and FastAlign approach, these alignments are comparable on public data sets.
Research on Low Resource Neural Machine Translation Based on Non-autoregressive Model
Authors: Xinlu Zhang , Hongguan Wu , Beijiao Ma , Zhengang Zhai 
Published: January 2022

Abstract
The autoregressive model can’t make full use of context information because of its single direction of generation, and the autoregressive method can’t perform parallel computation in decoding, which affects the efficiency of translation generation. Therefore, we explore a non-autoregressive translation generation method based on insertion and deletion in low-resource languages, which decomposes translation generation into three steps: deletion-insertion-generation. Therefore, the dynamic editing of the translation can be realized in the iterative updating process. At the same time, each step can be calculated in parallel, which improves the decoding efficiency. In order to reduce the complexity of data sets in non-autoregressive model training, we have trained Uyghur-Chinese training data with sequence-level knowledge distillation. Experiments on Uyghur-Chinese, English-Romanian distilled data sets and standard data sets verify the effectiveness of the non-autoregressive method.
Semi-supervised LDA and Multi-distance Metric Learning for Person Re-identification
Authors: Li Bin , Ding Haoyang , Zhou Mengjing 
Published: January 2022

Abstract
The problem of person re-identification has attracted a lot of attention in the field of machine vision. In practice, the non-overlapping sample images change drastically and the sample size is small, which makes the metric model overfitting phenomenon. In this paper, based on the k-NN and the sample normality property, we propose a resampling linear discriminant analysis (LDA) algorithm to suppress the local constraints caused by small samples, then train it to obtain the person re-identification metric learning model. A semi-supervised LDA algorithm with semi-supervised characteristics is developed by optimizing the inter-class scatter for weighting. A joint distance metric-based approach is also proposed to learn both the Mahalanobis distance and Euclidean distance. The improved algorithm is tested on the VIPeR and CUHK01 datasets, and the results indicate that, despite the change in the total number of training samples, the algorithm in this paper shows high recognition accuracy.
Intelligent LoRa NoC Applied in the Environmental Protection for Mu Us Desert
Authors: Liu Lu , Wang Huhu , Yang Yanfei 
Published: January 2022

Abstract
Intelligent NoC can be applied in the filed of environmental protection, since routing algorithm and network architecture possess advantages of speed and capacity. In this paper, wireless LoRa network is attempted to build for the environment of Mu Us Desert on the borders of Shaanxi and Inner Mongolia. Environmental sensors connected to LoRa network collects the environmental information, while intelligent routing algorithm helps to propagate and decide the environmental status. The intelligent NoC can contribute to complex data decision and build the NoC through multicast mapping.
Natural Scene Chinese Recognition Based on Deep Learning
Authors: Liu Xiaofei , Tong Pan , Yu Tao , Dong Andi , Yong Cuo 
Published: January 2022

Abstract
Text recognition is one of the research focuses in the field of computer vision. In recent years, many researchers have proposed recognition methods based on codec frameworks, such as CRNN, but most of these methods are based on English, and relatively few are based on Chinese. Chinese language, because of its large number of character types and contains a large number of similar characters, only using CNN to extract features will lose a large amount of information. Meanwhile, the method of giving combined attention is an emerging direction. In this paper, we study the recognition of the Chinese language and introduce attention to BiLSTM to obtain more contextual information. Compared with the original algorithm, the performance of this algorithm is improved on the synthetic dataset.
Research on Operating System Migration Method Based on Domestic Embedded Devices
Authors: Li Yan , Zhang Shiran , Li Jian , Xi Haikuo , Dong Tianying , Du Zexu 
Published: January 2022

Abstract
At present, Google’s Android system has the characteristics of open source, rich software functions, and good user experience, and has been highly recognized by a wide range of applications and the market. If the Loongson platform wants to make a difference in mobile terminals and other fields, it needs to support the Android system. However, although the native Android system supports multiple architectures, it only performs a lot of optimizations for the ARM architecture. For other architectures, Google only provides a generic version. This brings a huge challenge to the Loongson processor based on the MIPS architecture. Based on the analysis of the Android system, this paper studies the Android transplantation method of the Loongson 2K1000 platform in detail realizes the transplantation of the Android Lollipop system on the platform and has passed the verification of the Loongson 2K1000 platform.
Finger Vein Image ROI Extraction Based on Active Contour Method
Authors: Wang Yifan , Lu Huimin , Gao Ruoran , Wang Guizeng 
Published: January 2022

Abstract
Under the background of the novel coronavirus pneumonia outbreak in the world, unrestricted and contactless finger vein collection devices have significantly improved public health safety. However, due to the unfixed position of the finger and the open or semi-open characteristics of the acquisition device, it is inevitable to introduce plenty of factors that affect the recognition performance, such as low contrast, uneven illumination and edge disappearance. In view of these practical problems, we propose a method for ROI extraction of finger vein images that combines active contour method and morphological post-processing operations. This method starts from the local segmentation, and finally completes the acquisition of finger masks at the global level, and then combines some morphological operations to achieve precise extraction of finger masks. We designed and conducted plenty of comparison experiments on the proposed algorithm and the current mainstream finger vein image ROI extraction methods on three public available finger vein datasets. Experimental results show that our method accurately extracts the complete finger region mask and achieves the best matching accuracy on all datasets.
Research on Machine Vision Detection Method of Ship Sulfur Emission Based on Convolutional Neural Network
Authors: Han Yang , Li Guangzheng , Qin Qi , Wang Shipeng , Li Yuanhao 
Published: January 2022

Abstract
The traditional method of ship exhaust gas detection has the shortcomings of poor timeliness, blindness and hysteresis, which cannot meet the needs of marine supervision departments for comprehensive, efficient and real-time supervision of ship sulfur emissions. To solve this problem, a convolution neural network(CNN) based machine vision detection method for ship sulfur emission is introduced, and the plume characteristics of heavy sulfur black smoke ship exhaust are adaptively extracted by convolution operation. An improved LeNet-5 network model structure is proposed.By changing the activation function and increasing the depth of the model, the batch normalization operation is introduced to improve the accuracy and generalization ability of the model.The experimental results show that the detection accuracy of the model for heavy sulfur black smoke ships can reach 94.52%, which is better than the ordinary CNN network model and the VGG-16 network model.This method can quickly screen suspicious ships with excessive sulfur content, broaden the way for maritime supervision departments to detect ships under the background of “sulfur limit order”, and effectively improve the supervision efficiency of maritime personnel.
A Study of an Engine Anomaly Detection Model IForest-ADOA
Authors: Liu Liu , Xiao Min 
Published: January 2022

Abstract
In the environment of the Industrial Internet, automatic and effective anomaly detection methods are of great importance to ensure the safety and stability of engines. However, in practical engine applications, traditional machine learning anomaly detection methods often have limitations due to a large number of unmarked samples and high latitude data. In this paper, an engine anomaly detection model based on the ADOA model is proposed, combining the unsupervised learning iForest model with the semi-supervised learning ADOA model. Through the anomalous samples detected by iForest, the ADOA model is used to learn their features and patterns and perform clustering, to achieve the anomaly detection of the samples and improve its accuracy at the same time. Experiments based on a real engine dataset show that this model has good results in terms of anomaly recognition rate and anomaly misclassification rate for anomaly detection of high-dimensional data generated by real environments.
The DUNE vertical drift photon detection system
Authors: Paulucci L. 
Published: January 2022

Abstract
The Deep Underground Neutrino Experiment (DUNE) is a long-baseline neutrino experiment designed to mainly investigate oscillation parameters, supernova physics and proton decay. Its far detector will be composed of four liquid argon time projection chamber (LArTPC) underground modules, in South Dakota-USA, which will detect a neutrino beam produced at Fermilab, 1300 km away, where a near detector will be in place. The second DUNE far detector module, vertical drift, will be a single phase LArTPC with electron drift along the vertical axis with two volumes of 13.5 m × 6.5 m × 60 m dimensions separated by a cathode plane. The charge collection will be performed by two anode planes placed at the top and bottom of the module, each composed by stacked layers of a perforated PCB technology with electrode strips. The photon detection system (PDS) will make use of large size X-Arapuca tiles distributed over three detection planes. One plane will consist of a horizontal arrangement of double sided tiles installed on the high voltage cathode plane and two vertical planes, each placed on the longest cryostat membrane walls. A light active coverage of 14.8% over the cathode and 7.4% over the laterals should allow improvements in the low energy physics range that can be probed in DUNE, especially regarding supernova neutrinos (∼10 MeV). We present the initial characterization of the vertical drift PDS using a Monte Carlo simulation and preliminary studies on its reconstruction capabilities at the MeV scale. The information obtained with the PDS alone should allow determination of a neutrino interaction region with a precision of at least 65 cm for events with deposited energy above 5 MeV and the deposited energy can be reconstructed with precision better than 10%, both at the center of the volume.
Development of analog signal transmission in LAr for DUNE
Authors: Sacerdoti S. 
Published: January 2022

Abstract
The Deep Underground Neutrino Experiment (DUNE) is currently investigating a new prototype design for its second far detector module. The new concept proposes a vertical drift liquid argon time projection chamber, with a cathode at mid-height in the detector and anodes made of printed circuit boards, located at the top and bottom of the detector. In this context, the design of the photo-detection system needs to be revisited, opening a window of opportunity for further optimization and new developments. It is envisaged to distribute the photo-sensors (x-ARAPUCA) on the cathode surface. Such a system is required to operate within high-voltage surfaces, with both power supply and signal delivered using non-conductive materials. This article describes the on-going work to collect and read-out the signal of the photo-sensors, which are re-shaped into large tiles containing 160 SiPMs each. A new ganging scheme for the SiPMs is introduced. In particular, this article will focus on the proposed option to read out the sensors using an analog optical transmitter, that should ensure the transmission of the signals — with a wide dynamic range — to the outside of the cryostat to be digitized.
A model to characterize soil moisture and organic matter profiles in the permafrost active layer in support of radar remote sensing in Alaskan Arctic tundra
Authors: Bakian-Dogaheh Kazem , Chen Richard H , Yi Yonghong , Kimball John S , Moghaddam Mahta , Tabatabaeenejad Alireza 
Published: January 2022

Abstract
Organic matter (OM) content and a shallow water table are two key variables that govern the physical properties of the subsurface within the active layer of arctic soils underlain by permafrost, where the majority of biogeochemical activities take place. A detailed understanding of the soil moisture and OM profile behavior over short vertical distances through the active layer is needed to adequately model the subsurface physical processes. To observe and characterize the profiles of soil properties in the active layer, we conducted detailed soil sampling at five sites along Dalton Highway on Alaska’s North Slope. These data were used to derive a generalized logistics function to characterize the total OM and water saturation fraction behavior through the profile. Furthermore, a new pedotransfer function was developed to estimate the soil bulk density and porosity—information that is largely missing from existing soil datasets—within each layer, solely from the soil texture (organic and mineral properties). Given the currently sparse soil database of the Alaskan Arctic, these profile models can be highly beneficial for radar remote sensing models to study active layer dynamics.