This paper constructs a standard semantic reference system with a large number of scene semantic representations, then interprets and understands the reference system with low-level semantics, that is, commonsense, and compares the semantic representations of the standard reference system with the input text, forming the premise and foundation of machine analogy thinking. This paper discusses the method of using the scene framework to construct standard semantic reference system, describes the dynamic process of semantic recognition by analogy with standard reference system, describes the application examples of machine thinking oriented semantic representation, finally, summarizes the paper and explains the application prospect of natural language semantic representation method based on the scene framework.
{"title":"Natural Language Semantic Representation Method Based on the Scene Framework","authors":"Ping Zhu","doi":"10.1145/3446132.3446394","DOIUrl":"https://doi.org/10.1145/3446132.3446394","url":null,"abstract":"This paper constructs a standard semantic reference system with a large number of scene semantic representations, then interprets and understands the reference system with low-level semantics, that is, commonsense, and compares the semantic representations of the standard reference system with the input text, forming the premise and foundation of machine analogy thinking. This paper discusses the method of using the scene framework to construct standard semantic reference system, describes the dynamic process of semantic recognition by analogy with standard reference system, describes the application examples of machine thinking oriented semantic representation, finally, summarizes the paper and explains the application prospect of natural language semantic representation method based on the scene framework.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126928252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ismael Utitiaj, Paulina Morillo, Diego Vallejo-Huanga
The huge amount of textual information that exists on social networks added by users through comments, has aroused a great interest in companies and research groups, which seek to use this information to identify trends and acceptance levels of brands, products, and services. A technique to know the level of acceptance or rejection of a particular topic, in an automated way, is the Sentiment Analysis. Some informatics tools incorporate this technique, however, there are few contributions to texts in Spanish. It is because of the difficulty of identifying different contexts, dialects, complex grammatical structures, and semantic language variances in each region. This article presents a web tool for the analysis of sentiments in texts written in Spanish that include Ecuadorian dialect or idioms. The tool was developed in R-Shiny with an approach lexicon-based. The tool allows the customization of the lexicons based on context and facilitates the automatic download of tweets according to search criteria such as the place, dates, and topic. To evaluate the effectiveness of the application, their result was compared with two commercial tools (Azure Text Analytics and IBM Watson NLU) and a manual score carried out by a group of people. The tests include the analysis of three corpora created from tweets. The results show the effectiveness of the tool to identify the sentiment polarity, especially in texts that include dialects, colloquial words, and negative expressions.
{"title":"Sentiment Analysis Tool for Spanish Tweets in the Ecuadorian Context","authors":"Ismael Utitiaj, Paulina Morillo, Diego Vallejo-Huanga","doi":"10.1145/3446132.3446424","DOIUrl":"https://doi.org/10.1145/3446132.3446424","url":null,"abstract":"The huge amount of textual information that exists on social networks added by users through comments, has aroused a great interest in companies and research groups, which seek to use this information to identify trends and acceptance levels of brands, products, and services. A technique to know the level of acceptance or rejection of a particular topic, in an automated way, is the Sentiment Analysis. Some informatics tools incorporate this technique, however, there are few contributions to texts in Spanish. It is because of the difficulty of identifying different contexts, dialects, complex grammatical structures, and semantic language variances in each region. This article presents a web tool for the analysis of sentiments in texts written in Spanish that include Ecuadorian dialect or idioms. The tool was developed in R-Shiny with an approach lexicon-based. The tool allows the customization of the lexicons based on context and facilitates the automatic download of tweets according to search criteria such as the place, dates, and topic. To evaluate the effectiveness of the application, their result was compared with two commercial tools (Azure Text Analytics and IBM Watson NLU) and a manual score carried out by a group of people. The tests include the analysis of three corpora created from tweets. The results show the effectiveness of the tool to identify the sentiment polarity, especially in texts that include dialects, colloquial words, and negative expressions.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116049864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In excessive business competition, detecting weak signals is very important to anticipate future changes and events. The process of detecting weak signals is very challenging, and many techniques were proposed to automatize this challenge but still needs the intervention of experts’ opinion. Understanding those detected signals and their evolution in time is crucial to reveal the alertness of possible future events and warnings. For this reason, this paper proposes a new algorithm to strengthen weak signals into early warning signs. The proposed algorithm aims to monitor and track weak signals’ evolution within time. The output will be a list of early warning signs and visualization to illustrate their evolution in time. Finally, to adequately understand the early warning signs obtained and enhance their semantic alertness, we used Word2Vec modeling to provide semantically similar words to these warning signs and improve their contextual alertness. We tested this algorithm on a web news dataset of 2006-2007 to detect early warning signs related to the 2008 financial crisis ahead of time. We obtained prominent results in strengthening and monitoring the evolution of early warning signs related to this crisis.
{"title":"Monitoring Early Warning Signs Evolution Through Time","authors":"Manal El Akrouchi, H. Benbrahim, I. Kassou","doi":"10.1145/3446132.3446173","DOIUrl":"https://doi.org/10.1145/3446132.3446173","url":null,"abstract":"In excessive business competition, detecting weak signals is very important to anticipate future changes and events. The process of detecting weak signals is very challenging, and many techniques were proposed to automatize this challenge but still needs the intervention of experts’ opinion. Understanding those detected signals and their evolution in time is crucial to reveal the alertness of possible future events and warnings. For this reason, this paper proposes a new algorithm to strengthen weak signals into early warning signs. The proposed algorithm aims to monitor and track weak signals’ evolution within time. The output will be a list of early warning signs and visualization to illustrate their evolution in time. Finally, to adequately understand the early warning signs obtained and enhance their semantic alertness, we used Word2Vec modeling to provide semantically similar words to these warning signs and improve their contextual alertness. We tested this algorithm on a web news dataset of 2006-2007 to detect early warning signs related to the 2008 financial crisis ahead of time. We obtained prominent results in strengthening and monitoring the evolution of early warning signs related to this crisis.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116394040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To provide an effective test environment for ADAS(Advanced Driver Assistance Systems)functions and enrich the AEB(Autonomous Emergency Brake) test scenarios library. This paper refers to the existing methods of AEB test scenarios extraction at home and abroad. 114 natural driving samples data were collected, and 60 representative cases of rain or snow weather were selected manually. The K-means cluster analysis is adopted to analyze and classify the sample data, and obtained three AEB test scenarios with typical characteristics based on rain or snow weather. Built VTD and Matlab/Simulink co-simulation platform, to carry out the simulation test of AEB system. VTD and Matlab/Simulink co-simulation test proves that the scene extract in this paper are feasible.
{"title":"AEB Test Scene Extraction and Control Simulation based on Rain or Snow Weather","authors":"Xinyue Yang, Haiyun Gan, Ying Yan","doi":"10.1145/3446132.3446194","DOIUrl":"https://doi.org/10.1145/3446132.3446194","url":null,"abstract":"To provide an effective test environment for ADAS(Advanced Driver Assistance Systems)functions and enrich the AEB(Autonomous Emergency Brake) test scenarios library. This paper refers to the existing methods of AEB test scenarios extraction at home and abroad. 114 natural driving samples data were collected, and 60 representative cases of rain or snow weather were selected manually. The K-means cluster analysis is adopted to analyze and classify the sample data, and obtained three AEB test scenarios with typical characteristics based on rain or snow weather. Built VTD and Matlab/Simulink co-simulation platform, to carry out the simulation test of AEB system. VTD and Matlab/Simulink co-simulation test proves that the scene extract in this paper are feasible.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130629088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
RFID tag recognition technology is widely used in logistics and retail industry. In order to solve the problem of long time delay of information transmission in the process of multi tag recognition, a new anti-collision algorithm based on extended group of tag serial number is proposed after analyzing the key technologies of multi tag recognition, especially the advantages and disadvantages of tag anti-collision technology. The algorithm adds recursive grouping tag recognition method based on the Manchester code. This method changes the information interaction logic between the reader and the tag in the existing algorithm, makes great use of the advantages of the Manchester code, and makes use of the characteristics of the error code in the Manchester code to reverse the bit of the collision tag conflict, which effectively simplifies the communication between the reader and the writer to identify multiple tags. Delay[1]. Compared with the existing algorithms, this algorithm has a higher efficiency in the field of multi tag recognition technology. It can quickly identify multiple tags in a short time, effectively improve the capacity of the system, and ensure the stability and reliability of the tag recognition system.
{"title":"An anti-collision algorithm based on tag serial number extended grouping","authors":"Bowen Wang","doi":"10.1145/3446132.3446189","DOIUrl":"https://doi.org/10.1145/3446132.3446189","url":null,"abstract":"RFID tag recognition technology is widely used in logistics and retail industry. In order to solve the problem of long time delay of information transmission in the process of multi tag recognition, a new anti-collision algorithm based on extended group of tag serial number is proposed after analyzing the key technologies of multi tag recognition, especially the advantages and disadvantages of tag anti-collision technology. The algorithm adds recursive grouping tag recognition method based on the Manchester code. This method changes the information interaction logic between the reader and the tag in the existing algorithm, makes great use of the advantages of the Manchester code, and makes use of the characteristics of the error code in the Manchester code to reverse the bit of the collision tag conflict, which effectively simplifies the communication between the reader and the writer to identify multiple tags. Delay[1]. Compared with the existing algorithms, this algorithm has a higher efficiency in the field of multi tag recognition technology. It can quickly identify multiple tags in a short time, effectively improve the capacity of the system, and ensure the stability and reliability of the tag recognition system.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125607871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chao Lv, Fupo Wang, Jianhui Wang, Lei Yao, Xinkai Du
Learning the Semantic Textual Similarity (STS) is a critical issue for many NLP tasks such as question answering, document summarization and etc.. In this paper, we combine the Multiplicative LSTM structure with a Siamese architecture which learn to project word embeddings of each sentence into a fixed-dimensional embedding space to represent this sentence. Then these sentence embeddings can be used to evaluate the STS task. We compare with several similar architectures and the proposed method has achieved better results and is competitive with the best state-of-the-art siamese neural network architecture.
{"title":"Siamese Multiplicative LSTM for Semantic Text Similarity","authors":"Chao Lv, Fupo Wang, Jianhui Wang, Lei Yao, Xinkai Du","doi":"10.1145/3446132.3446160","DOIUrl":"https://doi.org/10.1145/3446132.3446160","url":null,"abstract":"Learning the Semantic Textual Similarity (STS) is a critical issue for many NLP tasks such as question answering, document summarization and etc.. In this paper, we combine the Multiplicative LSTM structure with a Siamese architecture which learn to project word embeddings of each sentence into a fixed-dimensional embedding space to represent this sentence. Then these sentence embeddings can be used to evaluate the STS task. We compare with several similar architectures and the proposed method has achieved better results and is competitive with the best state-of-the-art siamese neural network architecture.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114591225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xueyong Wu, Jie Li, Cheng Zhang, Yu Yang, Yachao Yang
Exact attitude estimation of an unmanned aerial vehicle (UAV) is the critical requirement for an autonomous and stable flight. Under dynamic conditions, the autopilot of a UAV cannot complete self-alignment and calculate its accurate attitude only by relying on an inertial measurement unit (IMU). This paper presents a dynamical alignment and estimation method for the horizontal attitude (the pitch and roll Euler angle) based on vision and inertial measurement units (IMU). Firstly, the horizontal attitude is estimated through image information of a visible light camera (hereinafter referred to as visual pose), and is used as the initial alignment input for IMU. Then the visual pose is corrected according to the normalized accelerometer output when UAV is stationary. Finally, Sage-Husa Adaptive Kalman Filter (SHAKF) is used for the fusion of the visual pose and the inertial attitude (the attitude calculated by the IMU). The simulation results show that the maximum estimation error of the UAV's horizontal attitude is within 3°, and the average estimation absolute error is less than 1°, which verifies the effectiveness of this method.
{"title":"Dynamical Alignment and Estimation for Horizontal Attitude of UAV Based on Vision and IMU.","authors":"Xueyong Wu, Jie Li, Cheng Zhang, Yu Yang, Yachao Yang","doi":"10.1145/3446132.3446161","DOIUrl":"https://doi.org/10.1145/3446132.3446161","url":null,"abstract":"Exact attitude estimation of an unmanned aerial vehicle (UAV) is the critical requirement for an autonomous and stable flight. Under dynamic conditions, the autopilot of a UAV cannot complete self-alignment and calculate its accurate attitude only by relying on an inertial measurement unit (IMU). This paper presents a dynamical alignment and estimation method for the horizontal attitude (the pitch and roll Euler angle) based on vision and inertial measurement units (IMU). Firstly, the horizontal attitude is estimated through image information of a visible light camera (hereinafter referred to as visual pose), and is used as the initial alignment input for IMU. Then the visual pose is corrected according to the normalized accelerometer output when UAV is stationary. Finally, Sage-Husa Adaptive Kalman Filter (SHAKF) is used for the fusion of the visual pose and the inertial attitude (the attitude calculated by the IMU). The simulation results show that the maximum estimation error of the UAV's horizontal attitude is within 3°, and the average estimation absolute error is less than 1°, which verifies the effectiveness of this method.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"5 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120868427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predictive maintenance uses improved Fuzzy Analytic Hierarchy Process (FAHP) model to calculate the weight of equipment failure and the comprehensive index information of equipment failure, to identify its deterioration trend, and to judge its trend, to calculate the comprehensive maintenance threshold, to generate maintenance decision information and to identify the equipment locations that need to be disposed of.
{"title":"An Intelligent Forecasting Method for Cigarette Equipment Maintenance Based on Fuzzy Mathematics Improved Analytic Hierarchy Process","authors":"Li-ming Zhu, Min Zhang, Qiang Zhang","doi":"10.1145/3446132.3446133","DOIUrl":"https://doi.org/10.1145/3446132.3446133","url":null,"abstract":"Predictive maintenance uses improved Fuzzy Analytic Hierarchy Process (FAHP) model to calculate the weight of equipment failure and the comprehensive index information of equipment failure, to identify its deterioration trend, and to judge its trend, to calculate the comprehensive maintenance threshold, to generate maintenance decision information and to identify the equipment locations that need to be disposed of.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127536689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a new network (CLGNet: Combined Local and Global information encoding Network) for human pose estimation based on commodity millimeter wave (mmWave) radar. Based on the benchmark model of ResNet, a global spatial information encoding module is introduced at the early stage of the network. This new module is expected to help learning the relationship between sparsely distributed human pose keypoints with internal relations. Our experimental results show that the addition of this structural module improves the prediction accuracy of human pose keypoints, especially on minor body parts, such as hands and feet.
本文介绍了一种基于商用毫米波雷达的人体姿态估计新网络(CLGNet: Combined Local and Global information encoding network)。在ResNet基准模型的基础上，在网络前期引入了全局空间信息编码模块。这个新模块有望帮助学习稀疏分布的人体姿势关键点与内部关系之间的关系。我们的实验结果表明，该结构模块的加入提高了人体姿势关键点的预测精度，特别是对身体的次要部位，如手和脚。
{"title":"CLGNet: A New Network for Human Pose Estimation using Commodity Millimeter Wave Radar","authors":"Qing Wang, Kai Wang, Wai Chen","doi":"10.1145/3446132.3446421","DOIUrl":"https://doi.org/10.1145/3446132.3446421","url":null,"abstract":"This paper introduces a new network (CLGNet: Combined Local and Global information encoding Network) for human pose estimation based on commodity millimeter wave (mmWave) radar. Based on the benchmark model of ResNet, a global spatial information encoding module is introduced at the early stage of the network. This new module is expected to help learning the relationship between sparsely distributed human pose keypoints with internal relations. Our experimental results show that the addition of this structural module improves the prediction accuracy of human pose keypoints, especially on minor body parts, such as hands and feet.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130709345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
TSP is one of the most famous problems in graph theory, and it is often used in the fields of urban infrastructure planning, logistics distribution, and transportation route arrangement. So far, no effective algorithm has been found to deal with this type of problem. Scholars believe that large-scale examples of this type of problem cannot be solved with an accurate algorithm, and an effective approximate algorithm for this type of problem must be sought. In order to gain a deeper understanding of the mTSP problem, this paper takes the national survey route planning as an actual case, and combines the improved circle algorithm and the Ant Algorithm to propose a specific solution. Based on the large amount of real data collected, the research route planning of the research team of Beijing M University traversing 30 ethnic minority autonomous prefectures and 120 ethnic minority autonomous counties was used as a calculation case, and various constraints were comprehensively considered to construct a cluster center based on 18 clusters. The TSP planning model with time constraints splits provinces based on clustering results, regenerates the split provinces and neighboring provinces into a new improvement circle for optimization, and finally obtains a survey time of at least 9.5 years, and integrates a specific survey route.
{"title":"Research on Survey Path Planning Based on mTSP Planning Model and Ant Algorithm","authors":"Jia Li, Tianci Jiao, Yan Wang","doi":"10.1145/3446132.3446144","DOIUrl":"https://doi.org/10.1145/3446132.3446144","url":null,"abstract":"TSP is one of the most famous problems in graph theory, and it is often used in the fields of urban infrastructure planning, logistics distribution, and transportation route arrangement. So far, no effective algorithm has been found to deal with this type of problem. Scholars believe that large-scale examples of this type of problem cannot be solved with an accurate algorithm, and an effective approximate algorithm for this type of problem must be sought. In order to gain a deeper understanding of the mTSP problem, this paper takes the national survey route planning as an actual case, and combines the improved circle algorithm and the Ant Algorithm to propose a specific solution. Based on the large amount of real data collected, the research route planning of the research team of Beijing M University traversing 30 ethnic minority autonomous prefectures and 120 ethnic minority autonomous counties was used as a calculation case, and various constraints were comprehensively considered to construct a cluster center based on 18 clusters. The TSP planning model with time constraints splits provinces based on clustering results, regenerates the split provinces and neighboring provinces into a new improvement circle for optimization, and finally obtains a survey time of at least 9.5 years, and integrates a specific survey route.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130759354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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