bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Impact of Super Resolution on detecting Covid-19 from CT Scans using VGG-16 Based Learning
Authors: Aburaed N , Panthakkan A , Al-Saad M , Al Mansoori S , Al Ahmad H 
Published: March 2021

Abstract
With the recent outbreak of the novel Coronavirus (COVID-19), the importance of early and accurate diagnosis arises, as it directly affects mortality rates. Computed Tomography (CT) scans of the patients’ lungs is one of the diagnosis methods utilized in some countries, such as China. Manual inspection of CT scans can be a lengthy process, and may lead to inaccurate diagnosis. In this paper, a Deep Learning strategy based on VGG-16 is utilized with Transfer Learning for the purpose of binary classification of CT scans; Covid and NonCovid. Additionally, it is hypothesized in this study that Single Image Super Resolution (SISR) can boost the accuracy of the networks’ performance. This hypothesis is tested by following the training strategy with the original dataset as well as the same dataset scaled by a factor of ×2. Experimental results show that SISR has a positive effect on the overall training performance.
Formation of the Structure and Training of a Multilayer Neural Network for the Analysis and Blood Glucose Level Prediction
Authors: Privezentsev D.G. , Zhiznyakov A.L. , Belyakova A.S. , Astafiev A.A. 
Published: March 2021

Abstract
The work aims to develop software for conducting research on the formation of the structure and training a multilayer neural network for analyzing and blood glucose level prediction. The paper analyzes the task, considers the tools, methods and algorithms for solving the problem. A model of an artificial neural network was investigated, the structure was developed and training was carried out. Summed up based on the results and assessed the effectiveness of such a system.
Low-code AutoML-augmented Data Pipeline – A Review and Experiments
Authors: Gain Ulla , Hotti Virpi 
Published: March 2021

Abstract
There is a lack of knowledge concerning the low-code autoML (automated machine learning) frameworks that can be used to enrich data for several purposes concerning either data engineering or software engineering. In this paper, 34 autoML frameworks have been reviewed based on the latest commits and augmentation properties of their GitHub content. The PyCaret framework was the result of the review due to requirements concerning adaptability by Google Colaboratory (Colab) and the BI (business intelligence) tool. Finally, the low-code autoML-augmented data pipeline from raw data to dashboards and low-code apps has been drawn based on the experiments concerned classifications of the “Census Income” dataset. The constructed pipeline preferred the same data to be a ground for different reports, dashboards, and applications. However, the constructed low-code autoML-augmented data pipeline contains changeable building blocks such as libraries and visualisations.
Decision-Making while Drilling Wells based on the Results of Modeling the Characteristics of Rocks using Probabilistic-Statistical Methods and Fuzzy Logic
Authors: Efendiyev Galib , Isayev Ramin , Piriverdiyev Igor 
Published: March 2021

Abstract
The current level of development of technical means and technologies creates favorable conditions for obtaining better information about the drilling process. To do this, it is necessary to analyze the information received, which in turn requires the use of methods that allow making decisions under uncertainty inherent in the drilling process. The operational information obtained in this case is very important and fundamental, which is of particular importance when drilling wells in areas for which there is no information or it is available in insufficient volume. The paper is devoted to increasing the efficiency of studying geological sections and improving the quality of information obtained in the process of drilling wells using probabilistic-statistical methods and fuzzy logic, which in turn contributes to improving the quality of decisions made. In two figures, a comparative analysis of changes in the properties of rocks and the rate of penetration was demonstrated, homogeneous intervals were identified according to the properties of rocks and drillability in general, which makes it possible to trace the intervals of possible complications.
Deep Learning for Binaural Sound Source Localization with Low Signal-to-noise Ratio
Authors: Zhao Fengnian , Li Ruwei , Pan Dongmei 
Published: March 2021

Abstract
A novel deep learning (DL) method is proposed for binaural sound source localization with low SNR. Firstly, the binaural sound signals are decomposed into several channels by using Gammatone filter. Secondly, the 4 feature parameters of Head-related Transfer Function, interaural time difference (ITD), interaural coherence (IC), interaural level difference (ILD), and interaural phase difference (IPD) are extracted. Thirdly, ITD and IC go through a Deep Belief Network (DBN) to determine the quadrant of the sound source and reduce the positioning range. Then, ITD, IC, ILD, and IPD go through a Deep Neural Network (DNN) to obtain the azimuthal angle within 90 degrees. Experimental results show that the proposed algorithm can solve the front-back confusion, and obtain a superior performance with lower complexity and higher precision under low SNR conditions.
Efficient Channel Attention Feature Fusion for Lightweight Single Image Super Resolution
Authors: Jiang Lingxiu , Zhou Yue 
Published: March 2021

Abstract
Recent advances in deep learning and convolution neural network have greatly improved the reconstruction performance of SISR compared with the traditional methods. However, complicated models and huge amount of parameters limit the application of those methods in real-world scenes. In our paper, we propose an efficient channel attention feature fusion method on the lightweight super-resolution network (ELSRN) for SISR. We reduce our network parameters through several modules, including binary cascading feature fusion. Besides, we propose to build efficient inverted residual block (EIRB) and stack several EIRBs to capture effective feature information of different scales. Last, we fuse multi-scale features in pairs step by step and finally refine final feature information with different scale features. Several experiments have proved that our EIRBs module and binary cascading method are effective and our network can achieve a great trade-off between reconstruction performance and model size.
Chinese Event Subject Extraction in the Financial Field Integrated with BIGRU and Multi-head Attention
Authors: Liu Yangwenhao , Liu Changhui , Wang Liya , Chen Zhe , Wu Yuan 
Published: March 2021

Abstract
Event subject extraction was to extract subjects of specific event types. For the traditional BiLSTM network, the threshold is complicated, the required parameters are many, and the time cost is high. This paper is oriented to the financial field and proposes a method of introducing a multi-head attention mechanism based on the BIGRU network to extract event subjects. First, the text is vectorized, and then the word vector obtained is input into the BIGRU network to learn the context features, and introduce a multi-head attention mechanism to extract the depth feature values of the text. Finally, a comparative experiment is conducted on the data set. The method in this paper achieves an Accuracy value of 80.47% and an F1 value of 89.18%. The result is better than the control group, indicating that the model proposed in this paper can effectively improve the accuracy of extracting Chinese event subjects.
Multi-objective Path Planning Based on K-D Fusion Algorithm
Authors: Kong Supeng , Chen Surong , Zhong Zhaoxi , Xu Bin , Shi Tianlei , Zhu Xiaohui , Yue Yong , Wang Wei 
Published: March 2021

Abstract
How to arrange sales staff to visit offline stores reasonably is a critical task in the Fast Moving Consumer Goods (FMCG) industry. Based on the K-means and Dijkstra algorithms (K-D fusion algorithm), this paper proposed an algorithm to automatically allocate offline stores for sales staff and optimize the visiting path, thereby improving management efficiency. A new initial cluster center selection approach was proposed for the K-means algorithm to select its initial clustering center with the consideration of outlier points. The sales staff’s visiting path to the store was planned by the Dijkstra algorithm. The performance of our K-D fusion algorithm was evaluated in terms of grouping rationality and path planning optimization. Experimental results show that our algorithm can comprehensively consider several factors such as the number of stores, store types, and geographical locations, and distribute the workload more evenly to all the sales staff. In addition, it can also optimize the visiting path for sales staff, which can effectively improve the efficiency of sales staff to visit offline stores.
Tensor Computations of Stochastic Dynamic Fields
Authors: Zhang Yinsheng 
Published: March 2021

Abstract
This article attempts to delve a kind of quantities—tensor for a stochastic dynamic domain, making its principles of computations such as gradient, divergence and interaction. The notation “stochastic dynamic field (SDF)”, proposed by the author, refers to such a dynamic field that it appears or annihilates, moves, acts or interacts at random. The study aims to consider not only randomness of vector fields in a continuous dynamic system, but also discontinuity with time variable t, i.e. more randomness for t, in a discontinuous dynamic system. To do so, representation forms of tensors areproperly transformed, which apply multiple-points tensor, and introduce the quantity of compound tensor like an expression of quantum state of combination of uncertainties. The work bears out some new results: (1) discontinuous mutation, even emerge or disappear, of a point in a tensor field, analyzed using a tensor with matrix structure, would not defer tensor computations of that field, while wouldn’t be computed commonly using accustomed representation of tensor with only a single-point structure. (2) Random quantity relations between phrases, no matter smooth nearby, non-connected or not, all can be computed. (3) Some classical physical laws analyzing field properties of space-relevant factors such as Euler equation and Gauss theorem are available in new forms with a random nature.
Study on the Segmentation Method of Multi-Phase CT Liver Tumor Based on Dual Channel U-Nets
Authors: Xu Changchang , Hu Dong , Zhang YaoDong , Pang Yuwei 
Published: March 2021

Abstract
As a common malignant tumor disease, hepatocellular carcinoma is the most common cancers in the world. The incidence of hepatocellular carcinoma in China is higher than that in the world. Therefore, it is very important for doctors to separate liver and tumor from CT images by means of computer-aided diagnosis and treatment. In this paper, a multiscale DC-CUNets network liver tumor segmentation method is proposed to enhance the fusion of multi-phase image features in CT, the scale of liver tumors, and the optimization of network training process. (1) A multistage CT liver tumor segmentation method based on two-channel cascaded U-Nets (DC-CUNets) is proposed. The liver was segmented using the first-order U-Net, and then the segmented area of interest of the liver was input into the second-order U-Net network to segment liver tumors. We designed two-channel U-Nets to learn the image characteristics of CT images in arterial and venous phases respectively, and to achieve two-channel feature fusion through feature cascade to improve the overall accuracy of liver tumor segmentation.(2) A multistage CT liver tumor segmentation method based on multiscale DC-CUNets was proposed. For the scale problem of liver tumors, we designed a two-layer multiscale void convolution module to obtain image features at different scales for large, medium and small tumors, and fuse the multiscale features at the output of the module. We have replaced the convolution layer of the fourth module in the second-order two-channel liver tumor segmentation U-Nets by the two-layer multiscale cavity convolution module to implement multiscale DC-CUNets.
Research on Application in Intelligent Vehicle Automatic Control System
Authors: Wang Yinghui , Sun Wenfu , Lu Yong 
Published: March 2021

Abstract
The traditional motor bridge circuit always has short circuit problems, and the smart car needs to be controlled by wire or remote control equipment. In order to study the application of automatic control technology in smart cars. Using the smart car model and simulation to study the smart car control system, with the mobile phone as the remote wireless control terminal, the smart car uses bluetooth communication and MCU as the intermediate bridge, the smart car achieves automatic direction control, voice control, gravity induction control, automatic tracking, automatic anti-collision and other functions. The motor circuit system is optimized by Simulation and experiment. It replaces the previous wired control and remote control equipment control, and effectively reduces the cost of equipment.
Crosstalk Analysis and Suppression of Single Chip SiC MOSFET Half-Bridge Circuit
Authors: Wang Yifan , Zhang Bin , Gong Jinlong , Wang Zun 
Published: March 2021

Abstract
For the hard-switch applications of the half-bridge circuit based on SiC MOSFET, the crosstalk generation mechanism of the half-bridge circuit in the switching on and off process is analysed. The influence of the driving resistance and stray inductance of driving circuit on the crosstalk is simulated and analysed. In the single-chip half-bridge circuit, the Miller clamp method with BJT + diode is used. By increasing the driving resistance and reducing the clamp circuit inductance, the crosstalk suppression effect can greatly improve. It is an effective way to optimize the clamping circuit and reduce the clamp circuit inductance without affecting the switch speed.
GIS-monitoring of Regional Transport Network Traffic as a Method to Study Commuting: Moscow Region Case
Authors: Mitroshin P A , Shitova Yu Yu , Shitov Yu A , Mitroshin A A , Vlasov D N 
Published: March 2021

Abstract
The paper is aimed to analyse the labour mobility in the region using GIS services. Within the framework of the project, a methodology for continuous automatic collection and accumulation of information on the state of the transport network and weather conditions was developed, tested and implemented. Based on this information, it is possible to analyse the important factors of the regional economy: temporary and financial losses of residents of the Moscow Region on the home-to-work paths, as well as identify patterns of traffic congestion from various factors. The first results of the analysis of the accumulated data are presented, which demonstrate the commuting effects in the regional transport network.
Multicriteria Algorithm for Multisensory Analysis of Coffee
Authors: Vianna Alexandre Magno da Rocha 
Published: March 2021

Abstract
The complexity of the new consumer market requires an equally complex investigative method to observe and analyze experiences, identifying neurophysiological patterns and their relationship with the choice of a product. The observation of the multisensory experience was made using fMRI, EEG and Fuzzy logic. For this project, the coffee consumption experience was analyzed and the neurophysiological responses observed, during the consumption of 3 different flavors, prepared in the same way. Based on the data and processes, a sensory mapping of the observer was generated in which the neurophysiological responses can be verified during the experiment. The data were processed and quantified in order to have a mathematical answer to the experience of each of the flavors and people, in addition to allowing an understanding of the importance of these standards for the selection of the winning option. Especially considering that a verbal response is not always the same as a neural response to the same stimulus.
Global virus lockdown and cybercrime rate trends: a routine activity approach
Authors: Govender I , Watson B W W , Amra J 
Published: March 2021

Abstract
Cybercrime perpetuates as a major threat to the reputation and economy of a nation. There is consensus in the media and academic journals alike, that cybercrimes have incremented over the years. During the coronavirus pandemic in the year 2020, a global lockdown imposed by governments forced people to be homebound, leading to a subsequent surge in online users. This resulted in cybersecurity risk hitting close to home, with an upsurge in cybercrimes. This article attempts to make sense of the elevated cybercrime landscape. For the last two decades, criminological research have attempted to explore variants of technology-enabled crime and have examined theories to account for offending. This study proposes ‘Routine Activity Theory’ to explain the magnitude of cybercrimes during this unprecedented period that reshaped people’s routine lifestyles. Routine activity theory predicts that changes in opportunity situations (increase in online users), potentially escalates the convergence of motivated offenders and suitable targets in the absence of capable guardianship. Guardianship (security measures) is pivotal in this theory, to protect the targets (online users) from offenders (cyber threat actors). Furthermore, this paper offers practical security measures, in light of the specified cyber-attacks during this unprecedented period. If this theory is empirically validated, it offers opportunities to mitigate the extent of cybercrimes with an upsurge of online users, and more so for cybercrimes in general.
Reliability assessment of the automatic plagiarism detection system for various editing patterns in documents containing complex mathematical notation
Authors: Bier Agnieszka , Sroczyński Zdzisław 
Published: March 2021

Abstract
The paper presents the idea and implementation details of a novel tool intended for detection of plagiarism in mathematical content of LATEX files. The proposed algorithm compares LTeX files and detects possible similarities in mathematical formulas, providing an objective similarity score. It offers two modes of comparison: direct (symbolic) mode and verbalized math mode, where the mathematical expressions are first verbalized to spoken language form and compared as such. The solution has been tested against identified various types of plagiarism specific to mathematical symbolic notation and LATeX features, and proved to be an efficient tool for plagiarism detection.
Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100
Authors: Yuen Man-Chung , Ng Sin-Chun , Leung Man-Fai 
Published: March 2021

AbstractPassive management contributes a more stable return than an active management strategy over the long term. Index-tracking is one of the passive investment strategies that attempt to replicate market indexes to reproduce the performance. Sparse index-tracking considers a subset of market index stocks to minimize the difference between the market index and the replicated index. In this paper, two metaheuristics are applied to solve this problem. The sparse index-tracking problem formed by the objective function of the empirical tracking error with the penalty values that result in an NP-hard problem. The penalty value is used to restrict the numbers of the considered stocks. To show the performance of the metaheuristics, various penalty values are investigated, and they produce approximation solutions to the index-tracking problem. Among them, particle swarm optimization shows better or statistically similar performance to GA in solving the sparse index-tracking problem.
Research on Network Security Protection of Application-Oriented Supercomputing Center Based on Multi-Level Defense and Moderate Principle
Authors: Yang Boxiong , Yu Ying , Wang Zeyu , Li Shelei , Xiao Heng , Gao Hualing , Liang Zhiyong , Zhou Xue 
Published: March 2021

Abstract
High Performance Computing (HPC), also known as Supercomputing, is widely applied in various fields, which have the advantage of high processing capability and mass storage. Various open business services bring great threat to the network and data security of HPC cluster. This paper proposes a feasible scheme to realize supercomputing information security through multi-layer defense and overheads control according to the experience of operation, management and maintenance of Supercomputer Center for many years. First, this paper puts forward the principle of moderate risk, and gives the principles of design and the implementation for the security protection of Supercomputing Center. Second, this paper presents a security system architecture in coordination with deep collaborative defense. Finally, this paper designs a 4-layer protection scheme on account of the latest network security solutions and the operation and maintenance characteristic of our own supercomputer. Experiments on multi-level defense and overheads control show that the designed scheme takes into account the cost and network security, and obtain prospective results., which is especially suitable for the network security protection of small-scale or medium-scale high-performance computing cluster for engineering application.
Attack Sample Generation Algorithm Based on Dual Discriminant Model in Industrial Control System
Authors: Zhou Wen , Cao Xueying , Li Xiaoming , Sha Yun , Chen Jianping , Yan Yong , Liu Xuejun , Kong Xiangmin 
Published: March 2021

Abstract
The research of intrusion anomaly detection in industrial control system is facing the problem of few attack samples. This paper proposes an attack data sample generation algorithm based on dual discriminant model. Firstly, a few attack samples are generated by multiple attacks based on the original data set. Secondly, the sparse matrix algorithm is used to expand the data set, and the original data features are distributed to each extended data to ensure the authenticity of the seed data. Finally, a large number of attack samples are generated by the dual discriminant model to complete the sample expansion. The experimental results of Mississippi SCADA natural gas pipeline data set and an industrial control system data set show that, based on keeping the data in a reasonable range, the algorithm can obtain an effectively expanded sample data set. SVM, Random Forest and XGBOOST are used to classify the dataset. The results show that AUC index is better than the negative sample data set generated by traditional GAN algorithm, and is similar to the initial negative sample index.
Description and Implementation of Production Factory Ontology
Authors: Hardt Filip , Žáček Martin 
Published: March 2021

Abstract
The article follows the previous work of the authors. The paper aims to elaborate ontology into a solid form using directly for the production factory. The authors divided the article into two parts. The first part explains the basics of conceptualization, and the second part is devoted to creating and describing ontology using the RDF model. As an example of ontology’s practical use, there are codes in the programming language #C.