. The rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations, with a likely significant impact on the labour market. Recent social-economic studies suggest that nearly 50% of occupations are at high risk of being automated in the next decade. However, the lack of granular data and empirically informed models have limited the accuracy of these studies and made it challenging to predict which jobs will be automated. In this paper, we study the automation risk of occupations by performing a classification task between automated and non-automated occupations. The available information is 910 occupations’ task statements, skills and interactions categorised by Standard Occupational Classification (SOC). To fully utilize this information, we propose a graph-based semi-supervised classification method named A utomated O ccupation C lassification based on G raph C onvolutional N etworks ( AOC-GCN ) to identify the automated risk for occupations. This model integrates a heterogeneous graph to capture occupations’ lo-cal and global contexts. The results show that our proposed method outperforms the baseline models by considering the information of both internal features of occupations and their external interactions. This study could help policymakers identify potential automated occupations and support individuals’ decision-making before entering the job market.
{"title":"Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks","authors":"Dawei Xu, Haoran Yang, Marian-Andrei Rizoiu, Guandong Xu","doi":"10.48550/arXiv.2209.02182","DOIUrl":"https://doi.org/10.48550/arXiv.2209.02182","url":null,"abstract":". The rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations, with a likely significant impact on the labour market. Recent social-economic studies suggest that nearly 50% of occupations are at high risk of being automated in the next decade. However, the lack of granular data and empirically informed models have limited the accuracy of these studies and made it challenging to predict which jobs will be automated. In this paper, we study the automation risk of occupations by performing a classification task between automated and non-automated occupations. The available information is 910 occupations’ task statements, skills and interactions categorised by Standard Occupational Classification (SOC). To fully utilize this information, we propose a graph-based semi-supervised classification method named A utomated O ccupation C lassification based on G raph C onvolutional N etworks ( AOC-GCN ) to identify the automated risk for occupations. This model integrates a heterogeneous graph to capture occupations’ lo-cal and global contexts. The results show that our proposed method outperforms the baseline models by considering the information of both internal features of occupations and their external interactions. This study could help policymakers identify potential automated occupations and support individuals’ decision-making before entering the job market.","PeriodicalId":244337,"journal":{"name":"International Conference on Advanced Data Mining and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133540153","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}
Pub Date : 2022-08-22DOI: 10.48550/arXiv.2208.10290
Peter Belcák, Roger Wattenhofer
. Owing to their versatility, graph structures admit representations of intricate relationships between the separate entities compris-ing the data. We formalise the notion of connection between two vertex sets in terms of edge and vertex features by introducing graph-walking programs. We give two algorithms for mining of deterministic graph-walking programs that yield programs in the order of increasing length. These programs characterise linear long-distance relationships between the given two vertex sets in the context of the whole graph.
{"title":"Deterministic Graph-Walking Program Mining","authors":"Peter Belcák, Roger Wattenhofer","doi":"10.48550/arXiv.2208.10290","DOIUrl":"https://doi.org/10.48550/arXiv.2208.10290","url":null,"abstract":". Owing to their versatility, graph structures admit representations of intricate relationships between the separate entities compris-ing the data. We formalise the notion of connection between two vertex sets in terms of edge and vertex features by introducing graph-walking programs. We give two algorithms for mining of deterministic graph-walking programs that yield programs in the order of increasing length. These programs characterise linear long-distance relationships between the given two vertex sets in the context of the whole graph.","PeriodicalId":244337,"journal":{"name":"International Conference on Advanced Data Mining and Applications","volume":"130 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113993062","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}
Pub Date : 2022-07-16DOI: 10.48550/arXiv.2207.07830
Poonam Sharma, Suman Banerjee
Now-a-days, emph{Online Social Networks} have been predominantly used by commercial houses for viral marketing where the goal is to maximize profit. In this paper, we study the problem of Profit Maximization in the twombox{-}phase setting. The input to the problem is a emph{social network} where the users are associated with a cost and benefit value, and a fixed amount of budget splitted into two parts. Here, the cost and the benefit associated with a node signify its incentive demand and the amount of benefit that can be earned by influencing that user, respectively. The goal of this problem is to find out the optimal seed sets for both phases such that the aggregated profit at the end of the diffusion process is maximized. First, we develop a mathematical model based on the emph{Independent Cascade Model} of diffusion that captures the aggregated profit in an emph{expected} sense. Subsequently, we show that selecting an optimal seed set for the first phase even considering the optimal seed set for the second phase can be selected efficiently, is an $textsf{NP}$-Hard Problem. Next, we propose two solution methodologies, namely the emph{single greedy} and the emph{double greedy} approach for our problem that works based on marginal gain computation. A detailed analysis of both methodologies has been done to understand their time and space requirements. We perform an extensive set of experiments to demonstrate the effectiveness and efficiency of the proposed approaches with real-world datasets. From the experiments, we observe that the proposed solution approaches lead to more profit compared to the baseline methods and in particular, the double greedy approach leads to up to $5 %$ improvement compared to its singlembox{-}phase counterpart.
{"title":"Profit Maximization using Social Networks in Two-Phase Setting","authors":"Poonam Sharma, Suman Banerjee","doi":"10.48550/arXiv.2207.07830","DOIUrl":"https://doi.org/10.48550/arXiv.2207.07830","url":null,"abstract":"Now-a-days, emph{Online Social Networks} have been predominantly used by commercial houses for viral marketing where the goal is to maximize profit. In this paper, we study the problem of Profit Maximization in the twombox{-}phase setting. The input to the problem is a emph{social network} where the users are associated with a cost and benefit value, and a fixed amount of budget splitted into two parts. Here, the cost and the benefit associated with a node signify its incentive demand and the amount of benefit that can be earned by influencing that user, respectively. The goal of this problem is to find out the optimal seed sets for both phases such that the aggregated profit at the end of the diffusion process is maximized. First, we develop a mathematical model based on the emph{Independent Cascade Model} of diffusion that captures the aggregated profit in an emph{expected} sense. Subsequently, we show that selecting an optimal seed set for the first phase even considering the optimal seed set for the second phase can be selected efficiently, is an $textsf{NP}$-Hard Problem. Next, we propose two solution methodologies, namely the emph{single greedy} and the emph{double greedy} approach for our problem that works based on marginal gain computation. A detailed analysis of both methodologies has been done to understand their time and space requirements. We perform an extensive set of experiments to demonstrate the effectiveness and efficiency of the proposed approaches with real-world datasets. From the experiments, we observe that the proposed solution approaches lead to more profit compared to the baseline methods and in particular, the double greedy approach leads to up to $5 %$ improvement compared to its singlembox{-}phase counterpart.","PeriodicalId":244337,"journal":{"name":"International Conference on Advanced Data Mining and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129418416","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}
Pub Date : 2022-02-07DOI: 10.1007/978-3-030-95405-5_29
Jillian Tallboys, Ye Zhu, S. Rajasegarar
{"title":"Identification of Stock Market Manipulation with Deep Learning","authors":"Jillian Tallboys, Ye Zhu, S. Rajasegarar","doi":"10.1007/978-3-030-95405-5_29","DOIUrl":"https://doi.org/10.1007/978-3-030-95405-5_29","url":null,"abstract":"","PeriodicalId":244337,"journal":{"name":"International Conference on Advanced Data Mining and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129610533","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}
Pub Date : 2021-09-21DOI: 10.1007/978-3-031-22137-8_13
Paul Grant, M. Islam
{"title":"Signal Classification using Smooth Coefficients of Multiple wavelets","authors":"Paul Grant, M. Islam","doi":"10.1007/978-3-031-22137-8_13","DOIUrl":"https://doi.org/10.1007/978-3-031-22137-8_13","url":null,"abstract":"","PeriodicalId":244337,"journal":{"name":"International Conference on Advanced Data Mining and Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125299965","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}
Pub Date : 2020-11-12DOI: 10.1007/978-3-030-65390-3_33
Jingwei Ma, Lei Zhu, Jiahui Wen, Mingyang Zhong
{"title":"HisRec: Bridging Heterogeneous Information Spaces for Recommendation via Attentive Embedding","authors":"Jingwei Ma, Lei Zhu, Jiahui Wen, Mingyang Zhong","doi":"10.1007/978-3-030-65390-3_33","DOIUrl":"https://doi.org/10.1007/978-3-030-65390-3_33","url":null,"abstract":"","PeriodicalId":244337,"journal":{"name":"International Conference on Advanced Data Mining and Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132876012","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}
Pub Date : 2020-11-12DOI: 10.1007/978-3-030-65390-3_48
Zhigao Zhang, Bin Wang
{"title":"Low-Light Image Enhancement with Color Transfer Based on Local Statistical Feature","authors":"Zhigao Zhang, Bin Wang","doi":"10.1007/978-3-030-65390-3_48","DOIUrl":"https://doi.org/10.1007/978-3-030-65390-3_48","url":null,"abstract":"","PeriodicalId":244337,"journal":{"name":"International Conference on Advanced Data Mining and Applications","volume":"28 13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117026242","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}
Pub Date : 2020-07-31DOI: 10.1007/978-3-030-65390-3_3
S. Saha, A. Soliman, S. Rajasekaran
{"title":"MSPP: A Highly Efficient and Scalable Algorithm for Mining Similar Pairs of Points","authors":"S. Saha, A. Soliman, S. Rajasekaran","doi":"10.1007/978-3-030-65390-3_3","DOIUrl":"https://doi.org/10.1007/978-3-030-65390-3_3","url":null,"abstract":"","PeriodicalId":244337,"journal":{"name":"International Conference on Advanced Data Mining and Applications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123339832","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}
Pub Date : 2020-07-10DOI: 10.1007/978-3-030-35231-8_35
Wei-xiang Li, Wei Jie, S. M. Zadeh
{"title":"Single Image Dehazing Algorithm Based on Sky Region Segmentation","authors":"Wei-xiang Li, Wei Jie, S. M. Zadeh","doi":"10.1007/978-3-030-35231-8_35","DOIUrl":"https://doi.org/10.1007/978-3-030-35231-8_35","url":null,"abstract":"","PeriodicalId":244337,"journal":{"name":"International Conference on Advanced Data Mining and Applications","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133276542","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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