The intelligent analysis and marketing prediction of high-tech enterprises based on artificial intelligence is a hot topic in the field. Most of the existing researches mainly focus on taking the internal structural features of the enterprise as the starting point to study the influencing factors of enterprise marketing trends. Different with previous studies, This research attempts to simulate the analyzing and decision processes of domain experts towards issues of enterprise operations by using artificial intelligent. One main challenge of the research is to simulate domain experts’ behaviors of analyzing multi-data including both structured and unstructured data, especially how to extract knowledge, patterns and import factors from unstructured data to support enterprise decisions. In order to solve the challenge mentioned above, an intelligent analysis framework MS-LSTM based on business management theory is proposed. Firstly, MS-LSTM collects, processes and analyzes multi-data by using an encoder strategy module, which contains more than 10 strategy models such as normalization, one-hot, distribution fitting, time series completion, semantic encoding, Bert, etc., providing high quality input for downstream tasks. Finally, a LSTM based time series processing model is proposed to make marketing prediction based on upstream processed multi-source data. Extensive experiments are conducted to verify the proposed model. Compared with traditional benchmark model, the proposed MS-LSTM could efficiently extract meaningful knowledge and patterns, which could be explained to a certain extent by business management theory, from multi-source data. The model has improved the accuracy of enterprise trend prediction by 19.3 times compared with state-of-art baselines, which further verify the application values of the research.
{"title":"Multi-data Fusion Based Marketing Prediction of Listed Enterprise Using MS-LSTM Model","authors":"Ziyang Pan, Zhishan Huang, Xiaowen Lin, Songxia Li, Huanze Zeng, Daifeng Li","doi":"10.1145/3446132.3446169","DOIUrl":"https://doi.org/10.1145/3446132.3446169","url":null,"abstract":"The intelligent analysis and marketing prediction of high-tech enterprises based on artificial intelligence is a hot topic in the field. Most of the existing researches mainly focus on taking the internal structural features of the enterprise as the starting point to study the influencing factors of enterprise marketing trends. Different with previous studies, This research attempts to simulate the analyzing and decision processes of domain experts towards issues of enterprise operations by using artificial intelligent. One main challenge of the research is to simulate domain experts’ behaviors of analyzing multi-data including both structured and unstructured data, especially how to extract knowledge, patterns and import factors from unstructured data to support enterprise decisions. In order to solve the challenge mentioned above, an intelligent analysis framework MS-LSTM based on business management theory is proposed. Firstly, MS-LSTM collects, processes and analyzes multi-data by using an encoder strategy module, which contains more than 10 strategy models such as normalization, one-hot, distribution fitting, time series completion, semantic encoding, Bert, etc., providing high quality input for downstream tasks. Finally, a LSTM based time series processing model is proposed to make marketing prediction based on upstream processed multi-source data. Extensive experiments are conducted to verify the proposed model. Compared with traditional benchmark model, the proposed MS-LSTM could efficiently extract meaningful knowledge and patterns, which could be explained to a certain extent by business management theory, from multi-source data. The model has improved the accuracy of enterprise trend prediction by 19.3 times compared with state-of-art baselines, which further verify the application values of the research.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"55 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":"125524025","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}
Early detection of Alzheimer's Disease (AD) is of great importance to the benefits of AD patients, including lessening symptoms and alleviating the financial burden of health care. As one of the leading signs of AD, changes of language capability can potentially be used for early diagnosis of AD. In this paper, I develop an automatic and accurate diagnostic model by using the linguistic characteristics of the subjects and hybrid word embedding. I detected linguistic features such as pauses, unintelligible words, repetitions, etc. from transcripts of interviews. Then I create a text embedding by combining word vectors from Doc2vec and ELMo. Moreover, by tuning hyperparameters of the machine learning pipeline (e.g., model regularization parameter, learning rate and vector size of Doc2vec, and vector size of ELMo), I achieve 91% classification accuracy and an Area Under the Curve (AUC) of 97% for distinguishing early AD from healthy subjects. Compared with the method which only uses word count, I improved the absolute detection accuracy by 10%, and the absolute AUC by 9%. Moreover, I study the stability of the model by repeating experiment and find out that the model is stable even though my training data is split randomly. My algorithms have high detection accuracy and are stable. This model could be used as a large-scale screening method for AD, as well as a complement to doctors’ detection of AD.
{"title":"Early Diagnosis of Alzheimer's Disease Using Hybrid Word Embedding and Linguistic Characteristics","authors":"Yangyang Li","doi":"10.1145/3446132.3446197","DOIUrl":"https://doi.org/10.1145/3446132.3446197","url":null,"abstract":"Early detection of Alzheimer's Disease (AD) is of great importance to the benefits of AD patients, including lessening symptoms and alleviating the financial burden of health care. As one of the leading signs of AD, changes of language capability can potentially be used for early diagnosis of AD. In this paper, I develop an automatic and accurate diagnostic model by using the linguistic characteristics of the subjects and hybrid word embedding. I detected linguistic features such as pauses, unintelligible words, repetitions, etc. from transcripts of interviews. Then I create a text embedding by combining word vectors from Doc2vec and ELMo. Moreover, by tuning hyperparameters of the machine learning pipeline (e.g., model regularization parameter, learning rate and vector size of Doc2vec, and vector size of ELMo), I achieve 91% classification accuracy and an Area Under the Curve (AUC) of 97% for distinguishing early AD from healthy subjects. Compared with the method which only uses word count, I improved the absolute detection accuracy by 10%, and the absolute AUC by 9%. Moreover, I study the stability of the model by repeating experiment and find out that the model is stable even though my training data is split randomly. My algorithms have high detection accuracy and are stable. This model could be used as a large-scale screening method for AD, as well as a complement to doctors’ detection of AD.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"71 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":"130655538","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}
Graph convolution network (GCN) is a Semi-supervised algorithm that applies the idea of convolution to graph structure data, and it is used for node classification tasks in graphs. Original algorithm only considers the characteristics and adjacency of the nodes in the graph, but fails to consider the association between label and simply represents the label as a one-hot vector. In this paper, we propose LC_GCN. This model contains a label convolution module based on the original GCN, and use it to get a better classifier. An open pre-trained word vector is used as the label feature, and we designed an algorithm to use the conditional probability of the association between label to generate adjacency matrix of labels to obtain the classifier by GCN. Then combine it with the original node GCN, and put the vector obtained by the node through the GCN into this classifier. Experimental results show that our proposed LC_GCN outperforms the existing algorithms.
{"title":"A Graph Convolution model considering label co-occurrence LC_GCN","authors":"Chaoshun Chang, Xiaoyong Li, Yali Gao","doi":"10.1145/3446132.3446155","DOIUrl":"https://doi.org/10.1145/3446132.3446155","url":null,"abstract":"Graph convolution network (GCN) is a Semi-supervised algorithm that applies the idea of convolution to graph structure data, and it is used for node classification tasks in graphs. Original algorithm only considers the characteristics and adjacency of the nodes in the graph, but fails to consider the association between label and simply represents the label as a one-hot vector. In this paper, we propose LC_GCN. This model contains a label convolution module based on the original GCN, and use it to get a better classifier. An open pre-trained word vector is used as the label feature, and we designed an algorithm to use the conditional probability of the association between label to generate adjacency matrix of labels to obtain the classifier by GCN. Then combine it with the original node GCN, and put the vector obtained by the node through the GCN into this classifier. Experimental results show that our proposed LC_GCN outperforms the existing algorithms.","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":"132820132","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 improve the prediction accuracy and stability of neural networks with random weights (NNRWs), we propose a novel ensemble NNRWs (E-NNRW) in this paper, which initializes its base learners by different distributions to improve their diversity. The final prediction results of the E-NNRW model are determined by these base learners through a voting mechanism, which minimizes the specific "blind zone" of a single learner, thus achieving higher prediction accuracy and better stability. Taking the random vector functional link network (RVFL), one of the most representative algorithms in NNRWs, as an example, we fully evaluate the performance of the proposed algorithm on nine benchmark classification problems. Extensive experimental results fully demonstrate the effectiveness of our method.
{"title":"Ensemble Neural Networks with Random Weights for Classification Problems","authors":"Ye Liu, Weipeng Cao, Zhong Ming, Qiang Wang, Jiyong Zhang, Zhiwu Xu","doi":"10.1145/3446132.3446147","DOIUrl":"https://doi.org/10.1145/3446132.3446147","url":null,"abstract":"To improve the prediction accuracy and stability of neural networks with random weights (NNRWs), we propose a novel ensemble NNRWs (E-NNRW) in this paper, which initializes its base learners by different distributions to improve their diversity. The final prediction results of the E-NNRW model are determined by these base learners through a voting mechanism, which minimizes the specific \"blind zone\" of a single learner, thus achieving higher prediction accuracy and better stability. Taking the random vector functional link network (RVFL), one of the most representative algorithms in NNRWs, as an example, we fully evaluate the performance of the proposed algorithm on nine benchmark classification problems. Extensive experimental results fully demonstrate the effectiveness of our method.","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":"131625845","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}
{"title":"Text Categorization by Multi-instance Multi-label and Momentum Stochastic Gradient Descent Strategy","authors":"Xiang Bao, Guifeng Liu, Manrong Wang","doi":"10.1145/3446132.3446158","DOIUrl":"https://doi.org/10.1145/3446132.3446158","url":null,"abstract":"","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"33 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":"128136917","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}
Nowadays, machine learning algorithms play a vital role in the field of artificial intelligence. However, it has been proved that deep convolutional networks (DCNs) are vulnerable to interference from adversarial examples. In this paper, we innovatively simulate natural textures by adding continuous noise to image subareas to generate adversarial examples, which can achieve up to 90% fooling rate on the object detection tasks (YOLOv3/Inceptionv3). The experimental results show that DCNs based on ImageNet dataset training relies too much on the feature aggregation of lower subareas in the classification task. It is instructive that when training DCNs, we need to consider not only the pursuit of accuracy but also the nature of model feature learning.
{"title":"Generating Adversarial Examples Based on Subarea Noise Texture for Efficient Black-Box Attacks","authors":"Zhijian Chen, Jing Liu, Hui Chen","doi":"10.1145/3446132.3446174","DOIUrl":"https://doi.org/10.1145/3446132.3446174","url":null,"abstract":"Nowadays, machine learning algorithms play a vital role in the field of artificial intelligence. However, it has been proved that deep convolutional networks (DCNs) are vulnerable to interference from adversarial examples. In this paper, we innovatively simulate natural textures by adding continuous noise to image subareas to generate adversarial examples, which can achieve up to 90% fooling rate on the object detection tasks (YOLOv3/Inceptionv3). The experimental results show that DCNs based on ImageNet dataset training relies too much on the feature aggregation of lower subareas in the classification task. It is instructive that when training DCNs, we need to consider not only the pursuit of accuracy but also the nature of model feature learning.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"25 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":"121735862","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}
Tibetan word segmentation and part-of-speech tagging are the most basic parts of Tibetan natural language processing, and its accuracy and performance have a crucial impact on many subsequent tasks. Considering the insufficiency of the pipeline model of word segmentation and part-of-speech tagging, this paper uses an integrated model of BiGRU-CRF word segmentation and part-of-speech tagging based on deep learning to simultaneously process two tasks of Tibetan word segmentation and part-of-speech tagging in one step. After conducting experiments on the Tibetan corpus collected in "Humanistic Tibet", the joint F1 value of Tibetan word segmentation and part-of-speech tagging was 92.48%.
{"title":"Research the Method of Joint Segmentation and POS Tagging for Tibetan using BiGRU-CRF","authors":"Zhixiang Luo, Jie Zhu, Zhensong Li, Saihu Liu","doi":"10.1145/3446132.3446395","DOIUrl":"https://doi.org/10.1145/3446132.3446395","url":null,"abstract":"Tibetan word segmentation and part-of-speech tagging are the most basic parts of Tibetan natural language processing, and its accuracy and performance have a crucial impact on many subsequent tasks. Considering the insufficiency of the pipeline model of word segmentation and part-of-speech tagging, this paper uses an integrated model of BiGRU-CRF word segmentation and part-of-speech tagging based on deep learning to simultaneously process two tasks of Tibetan word segmentation and part-of-speech tagging in one step. After conducting experiments on the Tibetan corpus collected in \"Humanistic Tibet\", the joint F1 value of Tibetan word segmentation and part-of-speech tagging was 92.48%.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"54 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":"122045632","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}
Satellite interference source location system is one of the important means of satellite communication anti-interference. High-precision satellite interference source positioning technology can accurately lock the location of the interference source and take measures to remove the interference. Time difference of arrival (TDOA) parameter estimation is a key link in the satellite interference source location system, and the accuracy of TDOA parameter estimation directly affects the location accuracy. This paper proposes a new TDOA parameter estimation algorithm that combines variational model decomposition (VMD) and wavelet threshold denoising (WTD). Firstly, the signal is adaptively decomposed into multiple components through the VMD algorithm to ensure the preservation of the original signal during the denoising process, then an improved WTD algorithm is used to remove the influence of noise in the satellite reception signal. Finally, accurate TDOA parameters are obtained. Numerical simulations show that the accuracy of the improved algorithm are better than conventional methods, thereby indirectly improving the accuracy of satellite interference source location.
{"title":"TDOA Parameter Estimation based on VMD-WTD in Satellite Interference Location","authors":"Shibing Zhu, Haifeng Shuai, Changqing Li, Rui Liu","doi":"10.1145/3446132.3446156","DOIUrl":"https://doi.org/10.1145/3446132.3446156","url":null,"abstract":"Satellite interference source location system is one of the important means of satellite communication anti-interference. High-precision satellite interference source positioning technology can accurately lock the location of the interference source and take measures to remove the interference. Time difference of arrival (TDOA) parameter estimation is a key link in the satellite interference source location system, and the accuracy of TDOA parameter estimation directly affects the location accuracy. This paper proposes a new TDOA parameter estimation algorithm that combines variational model decomposition (VMD) and wavelet threshold denoising (WTD). Firstly, the signal is adaptively decomposed into multiple components through the VMD algorithm to ensure the preservation of the original signal during the denoising process, then an improved WTD algorithm is used to remove the influence of noise in the satellite reception signal. Finally, accurate TDOA parameters are obtained. Numerical simulations show that the accuracy of the improved algorithm are better than conventional methods, thereby indirectly improving the accuracy of satellite interference source location.","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":"131375394","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}
Qiangwang Shen, Meng Ding, Shuqin Li, Wentao Du, Wenlong Zhao
Tibetan Jiuqi is a two-person chess type of Chinese ethnic minorities. In 2019, it was listed as one of the chess types of China Computer Game Championship. There are relatively few researches on Jiuqi's game strategy at home and abroad. The layout of the pieces of Jiuqi is generally divided into three stages: the opening, the middle game, and the endgame. This article is mainly based on the rules of the game to study the shape of the game in the opening and middle games. In the opening stage, the opening library is used to assist the game. In the middle stage, a strategy of forming a chess shape is proposed. This strategy is based on a single Dalian and further derives a more aggressive and flexible chess shape on this basis. The experimental results show that the strategy proposed in this article is effective.
{"title":"Research on Jiuqi Game Strategy Based on Chess Shape","authors":"Qiangwang Shen, Meng Ding, Shuqin Li, Wentao Du, Wenlong Zhao","doi":"10.1145/3446132.3446175","DOIUrl":"https://doi.org/10.1145/3446132.3446175","url":null,"abstract":"Tibetan Jiuqi is a two-person chess type of Chinese ethnic minorities. In 2019, it was listed as one of the chess types of China Computer Game Championship. There are relatively few researches on Jiuqi's game strategy at home and abroad. The layout of the pieces of Jiuqi is generally divided into three stages: the opening, the middle game, and the endgame. This article is mainly based on the rules of the game to study the shape of the game in the opening and middle games. In the opening stage, the opening library is used to assist the game. In the middle stage, a strategy of forming a chess shape is proposed. This strategy is based on a single Dalian and further derives a more aggressive and flexible chess shape on this basis. The experimental results show that the strategy proposed in this article is effective.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"18 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":"130426370","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}
With the development of artificial intelligence, virtual reality, and multimedia, numerous digital interactive learning resources are produced for the purpose of e-learning in which the virtual experiment plays a vital role. It provides not only an advanced tool but also an open platform with high-quality studying resources for learners to conduct research-oriented learning, self-directed experiment, and innovative practice. This method increases the quality of personnel training and adds extra energy and motivation to the reformation of practice teaching and laboratory construction. In this paper, a complete solution of virtual experiments is proposed which includes both overall design and module design. The overall design of the virtual experiment can be divided into three modules i.e. hardware platform design, software platform design, and unified access portal design, which are all illustrated. In the end, the results of virtual experiments are shown. These steps, which should require the generation of the final output from the styled paper, are mentioned here in this paragraph. Firstly, users have to run "Reference Numbering" from the "Reference Elements" menu and this is the first step to start the bibliography marking (it should be clicked while keeping the cursor at the beginning of the reference list). After the marking is complete, the reference element runs all the options under the "Cross Linking" menu.
{"title":"The Design and Development of Virtual Simulation Experiment for Online learning","authors":"Ran Wang, Jinglu Liu, Qijing Yu","doi":"10.1145/3446132.3446164","DOIUrl":"https://doi.org/10.1145/3446132.3446164","url":null,"abstract":"With the development of artificial intelligence, virtual reality, and multimedia, numerous digital interactive learning resources are produced for the purpose of e-learning in which the virtual experiment plays a vital role. It provides not only an advanced tool but also an open platform with high-quality studying resources for learners to conduct research-oriented learning, self-directed experiment, and innovative practice. This method increases the quality of personnel training and adds extra energy and motivation to the reformation of practice teaching and laboratory construction. In this paper, a complete solution of virtual experiments is proposed which includes both overall design and module design. The overall design of the virtual experiment can be divided into three modules i.e. hardware platform design, software platform design, and unified access portal design, which are all illustrated. In the end, the results of virtual experiments are shown. These steps, which should require the generation of the final output from the styled paper, are mentioned here in this paragraph. Firstly, users have to run \"Reference Numbering\" from the \"Reference Elements\" menu and this is the first step to start the bibliography marking (it should be clicked while keeping the cursor at the beginning of the reference list). After the marking is complete, the reference element runs all the options under the \"Cross Linking\" menu.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"85 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":"130349549","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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