bluuebunny/crossref_metadata_2025_split huggingface dataset:
Moving Object Detection Based on Weighted Kernel Norm and Saliency Constrained RPCA
Authors: Zhao Junhao , Jiang Zheng , Liu Bin , Zhang Ling 
Published: September 2022

Abstract
Focusing on the issue that the accuracy of object detection is reduced when the foreground target moves slowly and there is interference in the background, a moving object detection method based on weighted kernel norm and saliency constraint RPCA is proposed. In the new method, the weighted kernel norm is used to restore a relatively clean background, which is helpful to separate the slow moving target from the background. The l
1 norm is used to constrain the sparsity of moving objects, and the Frobenius norm is used to detect noise, and a saliency constraint is introduced to detect slow-moving objects. Experiments show that this method can effectively deal with the problems of slow foreground motion and background interference. Compared with the suboptimal algorithm, the average measured value F of the proposed algorithm is improved by 15%.
Numerical Simulation and Forecasting Model of Snow Drift Hazard in Yanchong Expressway with Embankment Form
Authors: Chen Yanxin , Chang Jiangfang , Fan Hongyu , Zhang Dali 
Published: September 2022

Abstract
To reasonably predict the snow drift hazard of expressway embankment in alpine mountainous area, in this paper, the computational fluid dynamics (CFD) numerical simulation method is used to analyze the distribution law of flow field of snow drift, the regression orthogonal design method is used to establish the prediction model of snow drift hazard in the expressway with the embankment form. Results show that different factors have different effects on snow drift hazard, the influence of embankment slope is the largest, followed by the coupling term of embankment slope and wind speed, the wind speed, the coupling term of embankment height and embankment slope, the coupling term of the mountain relative height and embankment slope, and the embankment height, while the influence of the distance between embankment and mountain-embankment slope coupling is the smallest. Numerical simulation and field observation have confirmed that the combined action of the terrain and embankment of Yanchong expressway Chicheng branch line leads to great changes in wind speed, resulting in uneven distribution of snow. A large amount of snow can be accumulated at the foot of the leeward slope of the mountain and the windward slope of the embankment, and there is less snow on the embankment pavement. By comparing the snow drift prediction model and CFD numerical simulation results, the error of the prediction model established in this paper is only 8.67%, which has high applicability. The research of this paper can provide a theoretical reference for the prediction and prevention of snow drift hazard on expressway embankment in alpine mountainous area.
An End-To-End NER Model with Explicit Boundary and Type Information
Authors: Feng Ying , Zhang Junchi , Chen Zhe , Wu Peiyan 
Published: September 2022

AbstractNamed Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP). The purpose is to locate and classify named entities with specific meanings, including the person and location. However, there are two problems with the existing methods: Firstly, the model cannot significantly distinguish boundaries and types of entities, especially if there are many types of entities. Secondly, the labels do not provide information on the differences in the boundaries and types of model entities. Therefore, the Explicit Boundary and Type Information (EBTI) models are used to address both issues to improve NER performance. Specifically, the boundary detection module uses for pure boundary recognition, and the type module uses to determine entity type information and then combine them to improve the performance of NER. Experiments are conducted on the widely used dataset CoNLL2003 to demonstrate the advantages of our method, with results comparable to the current state-of-the-art (SOTA).
A Real-Time Automated Defect Classification System Based on Two-Step Convolutional Neural Network
Authors: Wang Sen , Yan Shijia , Shen Qiang , Luo Cong , Li Lei , Ai Juan , Ding Shenglan , Xia Qing , Li Zhi , Chen Qilin , Li Shilin , Dai Hongwei 
Published: September 2022

Abstract
The increasing amount of defect images in semiconductor manufacturing process imposes the demand of classifying these images in real-time. In this paper we adopt a two-step deep learning based Convolutional Neural Network (CNN) to detect and classify the defect images from clean ones. The proposed method integrates the detection and classification process into one forward step, which takes image-level feature as well as empirical defect classification rules into account. In practice, the Real-Time Automated Defect Classification (RT-ADC) system was deployed in inline production process, which brings high efficiency; better wafer analysis and root cause determination for yield enhancement.
Clause Level Attention Network for Emotion Cause Pair Extraction
Authors: Chen Zhe , Zhang Junchi , Feng Ying , Hu Jie 
Published: September 2022

Abstract
The Emotion-Cause Pair Extraction task aims to identify one or more pairs of emotion-causes in a document, and earlier work has focused on the implicit interaction between ECPE and two auxiliary tasks Emotion Extraction and Cause Extraction. However, existing models cannot capture the complex connection between emotion and cause. To address this problem, we propose an end-to-end model. The model utilizes properties of graph attention networks (GATs) to model the bidirectional dependencies between emotion and cause clauses, and experimental results on a benchmark emotion cause corpus verify the feasibility of our approach.
Hiding Image within Image Based on Deep Learning
Authors: Yang Pan , Zhang Minqing , Wu Riming , Su Yunxuan , Guo Kaiyang 
Published: September 2022

Abstract
Information hiding technology is a technology that transmits secret information through a carrier, and is a research hotspot in the field of information security. The traditional information hiding algorithm relies on the meticulous design of human beings, and obtains the dense image through modification. With the development of deep learning, the integration of information hiding technology and deep learning has resulted in many information hiding technologies based on deep learning. Among them, image hiding has become a research hotspot due to its large steganographic capacity. Therefore, this paper reviews the information hiding technology based on deep learning. According to the difference of hidden models, it is analysed from four aspects: (1) information hiding model based on encoder-decoder; (2) information hiding model based on generative adversarial network; (3) information hiding model based on invertible network; (4) information hiding model based on neural network information hiding models for style transfer. Finally, these models are analysed and compared, and the future development direction is discussed and prospected.
Research on Error Correction Model of Transformer-Meter Relationship in Low Voltage Distribution Network Based on Combined Clustering Strategy
Authors: Shi Jian , Chen Kepeng , Wei Xunhu 
Published: September 2022

Abstract
At a time when distributed microgrids are gradually becoming the main form of renewable energy access, the distribution network plays an important role in serving the access and consumption of renewable energy, and is a powerful guarantee to promote the realization of the “double carbon” goal. However, due to the complex structure, frequent file changes, untimely governance and maintenance, the distribution station area cannot accurately perceive the ownership of transformer-meter relationship files. This paper presents an error correction method of transformer-meter relationship files in low-voltage substation area based on correlation analysis and clustering. Aiming at the application scenario of transformer-meter relationship file error under different busbars, the error correction is carried out by using the principle that the user voltage sequence curve of the error file has a lower similarity with the transformer voltage curve, and the voltage amplitude difference is larger. The results show that this method has high practicability and effectiveness.
Boolean Searchable Encryption Scheme Supporting Forward Security
Authors: Guo Sixu , Geng Huizheng , Su Li , Zhang Xinyue 
Published: September 2022

AbstractTo address the problem that most existing searchable encryption schemes do not support Boolean search for multiple keywords and cannot satisfy the forward security of indexes, a Boolean searchable encryption scheme supporting forward security (BSFS) is proposed in this paper. Two problems are solved by using techniques related to set theory and cryptographic knowledge: (1) The ability to implement Boolean search for multiple keywords based on traditional searchable encryption schemes; (2) The forward security of the index is satisfied by the clever construction of the index. The security analysis shows that the correctness, confidentiality and forward security of the scheme are guaranteed and that it has a high degree of practical applicability.
Dissolved Oxygen Prediction Based on PCA-LSTM
Authors: Tan Wenwu , Zhang Jianjun , Liu Xing , Yu Ziwen , Xiao Ke , Wang Li , Lin Haijun , Sun Guang , Guo Peng 
Published: September 2022

Abstract
Dissolved oxygen in surface water is an essential assessment of water quality. Predicting the concentration of dissolved oxygen in a basin is essential for the integrated use of water resources and the prevention and control of water pollution, as it enables the prediction of water quality trends in advance. In accordance with the cyclical and non-linear character of the variation of dissolved oxygen, we present a PCA-LSTM combined with a principal component analysis (PCA) method and a long and short-term memory network (LSTM) to estimate the dissolved oxygen concentration in the short period of time. Firstly, the dissolved oxygen data extracted from the water quality monitoring platform were preprocessed, and then 8 external variables, which retained most of the information, were converted into 5 new variables and put into the LSTM network for training. Finally, the predictions of the pre-processed training set data were compared using both the LSTM and PCA-LSTM models respectively. Experiments demonstrate that the PCA-LSTM model not only simplifies the structure of the proposed network, but also has more accurate prediction results than the conventional LSTM. Its mean absolute errors as well as the mean squared errors are improved by 2.71% and 9.03% respectively compared with the traditional LSTM model.
Machine Learning Based Testing Scenario Space and Its Safety Boundary Evaluation for Automated Vehicles
Authors: Zhang Yufei , Sun Bohua , Zhai Yang , Li Yaxin , Liang Hongyu , Liu Qiang 
Published: September 2022

Abstract
Autonomous vehicles (AVs) must be thoroughly evaluated before their release and deployment, so testing scenarios in simulations and closed facility tests are very important. The research presented in this paper focuses on the classification of scenario elements and the safety boundary of the scenario space. Scenario elements are divided into the static scenario elements and the dynamic scenario elements, and both of the two parts can be further divided into two types: the elements outside or inside the road. The importance weights of scenario elements are determined by Analytic Hierarchy Process (AHP) method and the effect transmission model to construct a scenario space. The relative speed, Time To Collision (TTC) and Time Headway (THW) are selected as risk assessment parameters. Support Vector Machine (SVM) is used to solve the safety boundary of the scenario space. Test results show that the classification of scenario elements is reasonable and the importance weights of scenario elements provide a basis for constructing the scenario space, and the regression lines of the safety boundary can distinguish dangerous driving states from safe ones.
Link Prediction of Complex Network Based on Eigenvector Centrality
Authors: Wang Li , Chen Chao , Li Hang 
Published: September 2022

Abstract
As one of the important methods connecting complex network and computer science, Link prediction deals with the most basic problems in information science. Therefore it is of great importance to probe into it. But how to improve the prediction accuracy is one of the focus problems we are facing. Most of the current link prediction methods are related to the indicators based on the similarity of nodes, and the importance of the neighbor nodes of nodes in the network is often determined by the similarity of nodes. indicators are ignored. Considering the aforementioned problems, we propose a link prediction algorithm based on eigenvector centrality calculated by node importance based on the eigenvector. The algorithm mainly uses the information of eigenvector centrality and considers Common Neighbor (CN), Adamic-Adar (AA) The similarity index of and Resource Allocation (RA), and the AUC value and the exact value are used as a reference for the pros and cons of the index, The results of simulation experiments are reported on two different network data sets, and the final results indicate that the algorithm based on eigenvector centrality is more accurate than the algorithm based on node importance in the link prediction of complicated networks.
Research on the Human-Computer Interaction Interface Design of Intelligent Luggage
Authors: Wang Xiyuan , Yi Xiya 
Published: September 2022

Abstract
It aims to combine the hierarchy of needs with the innovative design of the human-computer interaction interface of smart luggage to upgrade the usage efficiency and user experience of smart luggage. The design model of Maslow’s hierarchy of needs is adopted to discover the user needs of smart luggage in conjunction with various investigation methods, and a design model of the human-computer interaction interface of smart luggage is proposed based on Maslow’s hierarchy of needs. The design principles of the human-computer interaction interface of smart luggage are summarized and an interface is designed.
Characterization of irradiated RD53A pixel modules with passive CMOS sensors
Authors: Jofrehei A. , Backhaus M. , Baertschi P. , Canelli F. , Glessgen F. , Jin W. , Kilminster B. , Macchiolo A. , Reimers A. , Ristic B. , Wallny R. 
Published: September 2022

AbstractWe are investigating the feasibility of using CMOS foundries to fabricate silicon detectors, both for pixels and for large-area strip sensors. The availability of multi-layer routing will provide the freedom to optimize the sensor geometry and the performance, with biasing structures in poly-silicon layers and MIM-capacitors allowing for AC coupling. A prototyping production of strip test-structures and RD53A compatible pixel sensors was recently completed at LFoundry in a 150 nm CMOS process. This paper will focus on the characterization of irradiated and non-irradiated pixel modules, composed by a CMOS passive sensor interconnected to a RD53A chip. The sensors are designed with a pixel cell of 25 × 100 μm2in case of DC coupled devices and 50 × 50 μm2for the AC coupled ones. Their performance in terms of charge collection, position resolution, and hit efficiency was studied with measurements performed in the laboratory and with beam tests. The RD53A modules with LFoundry silicon sensors were irradiated to fluences up to1.0×1016neqcm2.
Testbeam performance results of bent ALPIDE monolithic active pixel sensors in view of the ALICE Inner Tracking System 3
Authors: Blidaru B.-M. 
Published: September 2022

Abstract
The ALICE Inner Tracking System has been recently upgraded to a full silicon detector consisting entirely of monolithic active pixel sensors, arranged in seven concentric layers around the LHC beam pipe. Further ahead, during the LHC Long Shutdown 3, the ALICE collaboration is planning to replace the three innermost layers of this new ITS with a novel vertex detector. The proposed design features wafer-scale, ultra-thin, truly cylindrical MAPS. The new sensors will be thinned down to 20–40 µm, featuring a material budget of less than 0.05% x/X
0 per layer, unprecedented low, and will be arranged concentrically around the beam pipe, as close as 18 mm from the interaction point.
Anticipating the first prototypes in the new 65 nm CMOS technology node, an active R&D programme is underway to test the response to bending of existing 50 µm thick ALPIDE sensors. A number of such chips were successfully bent, even below the targeted innermost radius, without signs of mechanical damage, while retaining their full electrical functionality in laboratory tests. The curved detectors were subsequently tested during particle beam campaigns, where their particle detection performance was assessed.
In this contribution, testbeam highlights from the data analysis of bent ALPIDE sensors, will be presented. It was proved that the current ALPIDE produced in the 180 nm CMOS technology retains its properties after bending. The results show an inefficiency that is generally below 10−4, independent of the inclination and position of the impinging beam with respect to the sensor surface. This encouraging outcome proves that the use of curved MAPS is an exciting possibility for future silicon detector designs, as not only the sensor can survive the bending exercise mechanically, but the enticing attributes that make it attractive for use in the inner tracking layers are comparable to the flat state.
Measurement of angular correlations of Compton-scattered gamma quanta from positron annihilation using GAGG:Ce scintillator matrices with single-side readout
Authors: Parashari S. , Bosnar D. , Kožuljević A.M. , Makek M. 
Published: September 2022

Abstract
We measured azimuthal correlations of the Compton-scattered gamma quanta from positron annihilation with two 8 × 8 matrices of Gadolinium Aluminum Gallium Garnet doped with Cerium. Each detector matrix contains 64 crystals of size 3 mm × 3 mm × 20 mm and it is read-out by a single Silicon Photo-multiplier (SiPM) array, with one SiPM matching one pixel. The studied single-side readout concept keeps the modules compact and cost-efficient on a large scale. Coincidence events were recorded using a Na-22 source placed between the modules. We clearly identify and reconstruct the Compton scattering events and we observe the excess of orthogonally scattered gammas over the ones with the parallel azimuthal angles. We present the measured azimuthal modulation factors for several kinematic selection criteria and different inter-pixel distances.
Timing techniques with picosecond-order accuracy for novel gaseous detectors
Authors: Tsiamis A. , Kordas K. , Manthos I. , Tsopoulou M. , Tzamarias S.E. 
Published: September 2022

Abstract
A simulation model is developed to train Artificial Neural Networks (ANN), for precise timing of PICOSEC Micromegas detector signals. The aim is to develop fast, online timing algorithms as well as minimising the information to be saved during data acquisition. PICOSEC waveforms were collected and digitised by a fast oscilloscope during a femtosecond-laser test beam run. A data set comprising waveforms collected with attenuated laser beam intensity, eradicating the emission of more than one photoelectron per light pulse from the PICOSEC photocathode, was utilised by a simulation algorithm to generate waveforms to train an ANN. A second data set of multi-photoelectron waveforms was used to evaluate the ANN performance in determining the PICOSEC signal arrival time, relative to a fast photodiode time-reference. The ANN timing performance is the same as the results of a full offline signal processing, achieving a timing precision of 18.3 ± 0.6 ps.
Phase transitions in the Ising model on a hierarchical random graph based on the triangle
Authors: Kotorowicz Monika , Kozitsky Yuri 
Published: September 2022

AbstractHierarchical graphs were invented to formalize heuristic Migdal–Kadanoff renormalization arguments. In such graphs, certain characteristic patterns (motifs) appear as construction elements. Real-world complex networks may also contain such patterns. Itzkovitz and Alon in 2005Phys. Rev.E71, selected five most typical motifs, which include the triangle. In 2011Condens. Matter. Phys.14, Kotorowicz and Kozitsky introduced and described hierarchical random graphs in which these five motifs appear at each hierarchy level. In the present work, we study the equilibrium states of the Ising spin model living on the graph of this kind based on the triangle. The main result is the description of annealed phase transitions in this model. In particular, we show that—depending on the parameters—the model may be in an unordered or ordered states at all temperatures, as well as to have a critical point. The key aspect of our theory is detecting the appearance of an ordered state by the non-ergodicity of a certain nonhomogeneous Markov chain.
Multi-wavelength off-axis digital holographic microscopy with broadly tunable low-coherent sources: theory, performance and limitations
Authors: Gorevoy Alexey , Polschikova Olga , Machikhin Alexander , Stoykova Elena 
Published: September 2022

Abstract
Multi-wavelength digital holographic microscopy (MDHM) is widely used in biological and industrial applications because of increased unambiguous height measurement range and the ability to measure concentration from the spectral dependence of phase delay. Acousto-optic tunable filters (AOTFs) provide the simultaneous selection of several bands with tunable central wavelengths to create a multiplexed hologram, but may limit the field of view (FOV) in off-axis holography because of the short coherence length of the filtered light. We analyzed the performance of the AOTF-based off-axis MDHM setup with a diffraction grating or a prism in the reference arm necessary to increase the efficiency of angular multiplexing. This allows varying the number of spectral channels selected simultaneously without setup realignment. Mathematical description relates the spectral bandwidth of the AOTF, tilt of the coherence plane induced by the angular dispersion of a prism or a grating, width of the FOV determined by interference pattern visibility, spatial resolution, and optimal intermediate wavelengths. We theoretically and experimentally demonstrated that the FOV may be expanded by changing the angle of light incidence on the AOTF and that the prism changes the wavelength dependence of the FOV. We validated this technique by single-shot acquisition of the height maps of the transparent test chart at four wavelengths with an error similar to that of four sequentially captured single-wavelength holograms. The results may be helpful for multiple applications of MDHM using spectrally tunable light sources.