bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance Metrics for Artificial Intelligence (AI) Algorithms Adopted in Prognostics and Health Management (PHM) of Mechanical Systems
Authors: Ochella Sunday , Shafiee Mahmood 
Published: March 2021

Abstract
Research into the use of artificial intelligence (AI) algorithms within the field of prognostics and health management (PHM), in particular for predicting the remaining useful life (RUL) of mechanical systems that are subject to condition monitoring, has gained widespread attention in recent years. It is important to establish confidence levels for RUL predictions, so as to aid operators as well as regulators in making informed decisions regarding maintenance and asset life-cycle planning. Over the past decade, many researchers have devised indicators or metrics for determining the performance of AI algorithms in RUL prediction. While most of the popularly used metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), etc. were adapted from other applications, some bespoke metrics are designed and intended specifically for use in PHM research. This study provides a synopsis of key performance indicators (KPIs) that are applied to AI-driven PHM technologies of mechanical systems. It presents details of the application scenarios, suitability of using a particular metric in different scenarios, the pros and cons of each metric, the trade-offs that may need to be made in choosing one metric over another, and some other factors that engineers should take into account when applying the metrics.
An Approach Based on Patterns for Synonymy Relations Detection
Authors: Tovar M , Flores-Petlacalco G , Reyes-Ortiz J A 
Published: March 2021

Abstract
Thesaurus, taxonomies, and ontologies are created as a way to represent knowledge. In recent years, the efforts to create them are focused on automatic knowledge extraction. For this purpose, research in the extraction of concepts and semantic relations is needed. Concepts or entities extracted from unstructured data has a became a challenge for the research communities. Texts, specifically, scientific papers contain knowledge where experts express their contributions to the science, including: techniques, methods and claims. In order to describe them, experts use synonyms, which are relevant to consider whereas it is desirable to extract such type of contribution form scientific text. This paper describes a method based on patterns for semantic relations extraction from scientific texts. The semantic relations correspond to synonymy. We describe pattern acquisition and relations extraction as phases to data evaluation. The evaluation shows promising results with 75% precision, 58% recall and 65% F-measure.
OpenVINO Deep Learning Workbench: Towards Analytical Platform for Neural Networks Inference Optimization
Authors: Demidovskij Alexander , Tugaryov Artyom , Kashchikhin Andrej , Suvorov Alexander , Tarkan Yaroslav , Mikhail Fedorov , Yury Gorbachev 
Published: March 2021

Abstract
Modern neural network methods make it possible to obtain qualitative results that open the colossal potential for integrating them into industrial applications. Therefore, there is a need for systems like the OpenVINO Deep Learning Workbench to analyze and optimize neural networks’ performance on target devices. As the primary tool for inference, the OpenVINO open environment is used, which provides a wide variety of options to speed up network execution on the target hardware.
Research on Contour Feature Extraction Algorithms for Arc-shaped Groove of Safety Seat Base
Authors: Liang Lipeng , Lu Yonghua , Zhu Huayu , Ye Zhibin , Chai Zhong 
Published: March 2021

Abstract
The spatial position of the seat base is complicated. The contour structure of arc groove is complex and its contour feature extraction is difficult. Aiming at the above problems, this paper presents an improved corner detection algorithm based on Curvature Scale Space (CSS), which uses adaptive local curvature and dynamic angle threshold to accurately extract corner points of arc groove contour. The correct corner positioning error of the algorithm proposed in this paper is only 0.99 pixel. The experimental results show that this method can effectively implement contour feature extraction.
TSW-FD: A Novel Temporal and Spatial Domain Weight Analysis of Feature Difference for Micro-Expression Spotting
Authors: Zhang Zhihao , Mo Fan , Zhao Ke , Chen Tong , Fu Xiaolan 
Published: March 2021

Abstract
The micro-expression spotting has recently attracted increasing attention from psychology and computer vision community, since embraced in the second facial Micro-Expression Grand Challenge (MEGC 2019). Different from the original feature difference (FD) analysis, in this paper, we proposed a novel temporal and spatial domain weight analysis of feature difference (TSW-FD) to achieve micro-expression spotting. The experimental results showed that TSW-FD improved 17.86% and 24.21% in F1-Score comparing to the FD in CASME II and SMIC-E-HS.
Unsupervised Mutual Mean Teaching with Heterogeneous Models for Person Re-Identification
Authors: Wang Wenlong , Wu Xiaofu , Zhang Suofei , Zhang Yuxin , Yan Jun 
Published: March 2021

Abstract
Person re-identification (Re-ID) aims at matching images of the same pedestrian under different cameras. State-of-the-art unsupervised domain adaptation (UDA) methods for person Re-ID transferred the learned knowledge from the source domain and further finetuned with pseudo-label target domain via clustering. To reduce the influence of noisy pseudo labels, the Mutual Mean-Teaching (MMT) framework was proposed by conducting pseudo label refinery to better model inter-sample relations in the target domain. Currently, MMT requires the use of the two same deep models for cooperative correction of noisy labels. In this paper, we extend the MMT framework to use two heterogeneous models for cooperative error correction. Experiments show that the proposed heterogeneous MMT framework can work well with two different deep models and its performance, however, is largely limited by the weaker model.
Algorithms for Modulation Recognition of Narrowband Power Line Carrier Communication Signals
Authors: Li Kun , Zhao Hongjun , Wang Yongjian 
Published: March 2021

Abstract
Automatic identification of modulated signals is an important technology of power line communication. Aiming at the problems of low signal recognition rate and characteristic parameter extraction in power line communication channel, this paper uses amplitude variance value of wavelet transform and higher order cumulant as the identification parameter, and designs a signal recognizer based on improved support vector machine. Under the condition of power line channel environment, the recognizer of this paper is less than the existing recognition method in the computational complexity, and has good robustness to power line noise. At the same time, it avoids the shortcomings of traditional neural network such as under-learning and over-learning. The simulation results show that when the SNR is 5 dB, power line communication signals through the recognizer, which the correct identification rate can reach 91%.
GSEMT: A Gene Set Enrichment Analysis Method Based on Mantel Test
Authors: Yu Na 
Published: March 2021

Abstract
Gene expression changes constantly with the occurrence and progression of diseases. The vast available gene expression data makes it possible for clinical researchers to understand the link between genotypes and phenotypes. However, it is still not an easy task because the information contained in the gene expression matrix is sparse. Gene set enrichment analysis is a powerful tool to meet the challenge of identifying complicated differential information underlying pathways. In this paper, we propose a method, called GSEMT, for gene set enrichment analysis by testing the correlation between a sample similarity matrix and a phenotype dissimilarity matrix. We implement experiments on knowledge-based gene sets and gene expression datasets for hepatocellular carcinoma. We justify the effectiveness and advantage of GSEMT by carrying out comparison studies. GSEMT outperforms GSEA and GSNCA in the classification performance on an experiment dataset and an independent validation dataset. The results show GSEMT is a useful alternative for gene set enrichment analysis.
Development of Multi-layer Soil Model for Yanmenguan Converter Station Based on Wenner and Magnetotelluric Method
Authors: Cheng Xiaosheng , Gao Jinwen , Feng Lijun , Hao Chenglong , Zhao Yuting , Cai Xinjing 
Published: March 2021

AbstractIn order to assess the effects of Yanmenguan HVDC grounding electrode on buried metal facilites, it is necessary to build up the soil model of Yanmenguan converter station. Soil resistivity was measures by Wenner and magnetotelluric method. Then the soil structure of Yanmenguan converter station was inversed based on the experimental data. It was shown that the soil structure of Yanmenguan converter station was three-layer horizental soil. The resisitivity of three layers was 101.7·m, 1037.5·m and 76.4·m. The thickness of three layers was 400m, 4600m and infinite.
A Low-bit And Data-conversion-free Memristive Spiking Computing Network
Authors: Qiao Guanchao , Hu Shaogang 
Published: March 2021

Abstract
Memristor crossbar holds promising potential in massively parallel data processing, as computing tasks beyond vector-matrix multiplication typically requires additional complex data conversion modules. In this work, we report a memristive spiking computing network (MSCN) without data conversion. Two memristor crossbars are used to perform vector-matrix multiplication, while spiking neurons are used to integrate the signals from each column of the memristor crossbars. Input and output signals are binary spikes (spiking or non-spiking); thus data conversions between the analog and digital domains are avoided. The MSCN is suitable for accelerating artificial neural network (ANN). A multi-layer perception (MLP) consisting of four layers is simulated based on the analog MSCN, and recognition accuracy of 98.55% under the MNIST test set is achieved. However, in the practical memristor, it is challenging to adjust conductance analogously and even challenging to implement multi-bit conductance states. Therefore, the memristor conductance states are further reduced to 3 bits and 1 bit. Encouragingly, recognition accuracies just slightly decrease to 98.36% and 97.96% for 3-bit and 1-bit conductance states, respectively, which is cost-effective considering the hardware reliability improvement.
Building a Catalogue of ISO/IEC 25010 Quality Measures Applied in an Industrial Context
Authors: Falco Mariana , Robiolo Gabriela 
Published: March 2021

Abstract
Measuring quality is extremely important while developing a software product, but there is a lack of knowledge of which are the ISO/IEC 25010 quality measures that are currently being used in the industry. Through a literature analysis of 27 studies, the present article introduces a catalogue of 269 quality measures applied in an industrial setting, which is formed by a subset of 81 metrics defined by GQM approach, a subset of 81 specific metrics for quality characteristics, and a subset of 86 metrics defined through ISO/IEC 25023 and 21 through IT-CISQ. In conclusion, it can be said that GQM is the most widely used method, regardless of standards. Likewise, Maintainability, Performance Efficiency and Usability are the quality characteristics that have shown the highest degree of interest because they are more receptive to the end user.
Virtual Streaming Computing Systems with Architecture Based on the Neural Network Information Model
Authors: Myshev A V , Slavin O A 
Published: March 2021

Abstract
The paper reveals the functional, structural and logical organization of new generation virtual streaming computing systems. Such systems allow realizing a new paradigm of computational intelligence and accessing new intelligent computing capabilities. The paradigm that allows realizing new advantages of virtual streaming computing systems is considered. These capabilities are implemented based on information models of a neural network and non-traditional principles for the development and implementation of various forms of computing. New forms of computing make it possible to control the mechanisms of parallelism, virtualization and intellectualization on the information dynamics of objects in the computing environment.
VoLTE problem location method based on big data
Authors: Chen Xinwei , Zhou Jun , Liu Keqing 
Published: March 2021

Abstract
This paper mainly introduces the key information extracting method from User Equipment (UE) test data, the synchronous algorithm of various interfaces X Detailed Record (XDR) data, the key information filling technique of multi interfaces X Detailed Record (XDR), the analytical procedure based on big data is descript in details. An example is given to explain that more accurate reason for Voice over Long-Term Evolution (VoLTE) call not connected can be found with big data from core network, compared with UE data only. With this method VoLTE problem can be quickly solved, it is very useful for wireless operator to improve the user voice service awareness rapidly.
Weight Calculation of GSM-R Network Quality of Service Evaluation Indexes Based on Analytic Hierarchy Process and Entropy Weight Method
Authors: Ma Liangde , Zuo Zihui , Zhao Bo , Li Lincan 
Published: March 2021

Abstract
In order to assign an objective and compre-hensive weight to each index for evaluating the GSM-R network quality of service, this paper used the combination of subjective and objective weight value to overcome the singularity of determining the weight from one aspect. The entropy weight method was used to determine the objective weight, which was assigned to each index by the entropy calculation. The subjective weight was calculated on the basis of the improved analytic hierarchy process (AHP) and the scale construction method was used to construct the judgment matrix, which reduced the computational complexity and redundancy. In addition, three kinds of calculation methods were proposed for combining multiple experts’ opinions. Combining the subjective and objective weight vectors obtained by different combination methods, the results of multiple integrated weight vectors were obtained. Then the average value was calculated for each index. By comparing the correlation coefficient between each integrated weight vector and the average weight vector, a better multiple combining calculation method was determined to calculate the weight of each index in the evaluation system. This paper combined the actual test data of ZX section of XL high-speed railway and the experts’ questionnaires to obtain the weight assignment of GSM-R network quality of service indexes. It can be seen from the results that the voice quality, switching success ratio and average handover delay account for a large proportion, which is in line with reality. The judgment method of weight proposed in this paper is objective and feasible.
Decision-making Algorithm for Improving the Lifetime of Signal Equipment
Authors: Dong Danyang , Yu Yi , Yuan Jianjun , Yilixiati Alimasi , Chu Pengzi 
Published: March 2021

Abstract
The safety of equipment operation is important for the operation of rail transits. The lifetime of equipment and its improvement methods have become one of the key issues in the operation and maintenance of rail transits. Based on the fuzzy comprehensive evaluation process and evaluation result data, the decision-making algorithm for improving the lifetime of the equipment was discussed, including the strategy recommendation algorithm based on assessed data and the strategy matching algorithm based on case reasoning. Taking the actual data of onboard ATC equipment of Line 11 in a city as an example, the effectiveness of the two methods was verified. The methods in the study can support the decision-making process for maintenance departments.
Analysis on Stress Screening of Components in Digital Background
Authors: Wei Juan , Sun Hongbo , Chen Jie 
Published: March 2021

Abstract
This paper introduces various common defects and corresponding screening methods of electronic components. Aiming at the temperature cycle and random vibration methods with good screening effect, the calculation formula of accelerating factor and screening strength in highly accelerated stress screening (HASS) test is introduced. Based on the above discussion, the stress screening test steps of electronic components are summarized to guide the selection of reasonable stress screening items and levels for electronic components.
A Very Lightweight Encryption Method Based on Multiple Substitution Tables
Authors: Henschen L , Lee J 
Published: March 2021

Abstract
We present a very lightweight encryption method based on multiple substitution tables. The method does not use sophisticated integer arithmetic and is therefore suitable for small embedded systems and other applications using simple processing elements. Plain text is encrypted one byte at a time. Each byte is replaced by the substitution element in the current table, and the plain text byte also determines the table to be used for the next byte. Encrypting and decrypting one byte involves only a table look-up, no arithmetic or bit manipulation. The same plain text byte has very small probability of being encrypted the same way twice in a row. We also present additional techniques for making the method secure against attacks when used in applications where the messages may have special or restricted forms.
Model for the Implementation of Security in Wi-Fi Networks for SMEs
Authors: Vega Diego , Lozano Andrés , Blanco Fabián , Simanca Fredys 
Published: March 2021

Abstract
In the last decades, the computerized handling of data has played a fundamental role in different economic areas, since there are several technological instruments that help to speed up a number of processes, so it is of utmost importance to learn how to use these tools, otherwise the information would be compromised. This paper aims to present a model for small and medium enterprises, through a pro-cess of identification and analysis of security measures in companies, based on the study of each company and in relation to the size of each organization, allow-ing to know the concepts of network monitoring that are applied in the creation of strong security policies for their facilities.
Analytical and Instrumental Framework for The Analysis and Resolution of Stakeholder Interest Conflict Using Cognitive Maps
Authors: Avdeeva Z K , Kovriga S V 
Published: March 2021

Abstract
The statement of the basic control problem of a situation with many active stakeholders using cognitive maps is presented. Approaches to the analysis and resolution of interest conflicts in such situations based on the basic control problem are proposed. These approaches make it possible to take into account different interests, identify contradictions and, if possible, find ways to overcome these contradictions by choosing different strategies for interaction between stakeholders. The problems systematization of conflict analysis and control in such situations at different phases of the conflict is presented. The systematization purpose is to organize monitoring, analysis and control of conflict situations using cognitive maps and scenario modeling tools based on them.
Public Sentiment Big Data Query Processing and Optimization with Unified Storage of Source and Meta Data
Authors: Yan Donglei , Li Jiaxin , Lei Shengnan , Tang Junri , Kou Kaiqi , Wang Zhiqiong 
Published: March 2021

Abstract
Public sentiment big data has the characteristics of mass, multi-source, heterogeneity and multi-mode. At present, it mostly adopts separate storage strategies and has low storage and query efficiency. To this end, a distributed storage model is proposed in which structured and unstructured data correspond and are stored uniformly. By establishing a unified storage framework for source metadata, the original source data and characteristic metadata are stored uniformly according to different storage methods. Based on the above storage architecture, a hierarchical index structure is proposed to improve the efficiency of big data query. The unified storage model of source metadata is compared with the common storage methods, and is in the leading position in terms of Block number and query processing efficiency.
Multi-user Fuzzy Keyword Searchable Encryption Scheme Based on Certificateless Cryptosystem
Authors: Yang Xiaodong , Wang Jiaqi , Tian Tian , Wang Xiuxiu 
Published: March 2021

Abstract
Nowadays, the application prospect of cloud storage is more and more extensive, and the security problem of cloud storage has become one of the hotspots of research. Searchable encryption can make full use of the huge computing resources of cloud servers and solve the problem of secure search after data encryption and upload. However, the existing searchable encryption schemes have the problems of high computational cost, low security, and insufficient flexibility in the way of searching keywords. In order to solve these problems, we propose a new certificateless multi-user fuzzy keyword searchable encryption scheme, which introduces the key distribution center, effectively reduces the computational cost, solves the problem of certificate management and key escrow, and uses the optimized wildcard technology to realize the fuzzy search of multiple users, which is not only brings convenience to users, but also realizes flexible search methods. Finally, the analysis results show that our scheme has indistinguishable security under keyword guessing attacks. Simulation experiments and performance analysis results show that our scheme has higher computational efficiency.
Identity-based Data Possession Verification Scheme for Multi-Replica Storage in Cloud
Authors: Yang Xiaodong , Wang Xiuxiu , Li Kaibing , Li Xixi 
Published: March 2021

Abstract
In the cloud storage environment, how to efficiently and dynamically complete the integrity verification of multi-user and multi-replica data is a challenging problem. Based on the bilinear mapping signature mechanism and the multi-branch authentication tree, we propose an identity-based integrity verification scheme of multi-replica data for cloud, which solves the problem of large public key storage and management overhead in signature algorithm based on traditional public key cryptosystems. The proof shows that our scheme satisfies the robustness and has the function of protecting privacy. The results of analysis indicate that, compared with the existing similar schemes, our scheme has higher communication efficiency and computing performance.