International Conference on Advanced Data Mining and Applications最新文献

Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks 自动化与否?基于图神经网络的职业风险识别

International Conference on Advanced Data Mining and Applications Pub Date : 2022-09-06 DOI: 10.48550/arXiv.2209.02182

Dawei Xu, Haoran Yang, Marian-Andrei Rizoiu, Guandong Xu

{"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}

引用次数: 0

Deterministic Graph-Walking Program Mining 确定性图行走程序挖掘

International Conference on Advanced Data Mining and Applications Pub Date : 2022-08-22 DOI: 10.48550/arXiv.2208.10290

Peter Belcák, Roger Wattenhofer

引用次数: 0

Profit Maximization using Social Networks in Two-Phase Setting 两阶段环境下社会网络的利润最大化

International Conference on Advanced Data Mining and Applications Pub Date : 2022-07-16 DOI: 10.48550/arXiv.2207.07830

Poonam Sharma, Suman Banerjee

{"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}

引用次数: 0

Identification of Stock Market Manipulation with Deep Learning 用深度学习识别股票市场操纵

International Conference on Advanced Data Mining and Applications Pub Date : 2022-02-07 DOI: 10.1007/978-3-030-95405-5_29

Jillian Tallboys, Ye Zhu, S. Rajasegarar

引用次数: 1

Clique percolation method: memory efficient almost exact communities 团渗透法:内存效率高，几乎精确的社团

International Conference on Advanced Data Mining and Applications Pub Date : 2021-10-04 DOI: 10.1007/978-3-030-95408-6_9

Alexis Baudin, Maximilien Danisch, Sergey Kirgizov, Clémence Magnien, M. Ghanem

引用次数: 2

Signal Classification using Smooth Coefficients of Multiple wavelets 基于多小波平滑系数的信号分类

International Conference on Advanced Data Mining and Applications Pub Date : 2021-09-21 DOI: 10.1007/978-3-031-22137-8_13

Paul Grant, M. Islam

引用次数: 1

HisRec: Bridging Heterogeneous Information Spaces for Recommendation via Attentive Embedding hrecc:通过关注嵌入桥接异构信息空间的推荐

International Conference on Advanced Data Mining and Applications Pub Date : 2020-11-12 DOI: 10.1007/978-3-030-65390-3_33

Jingwei Ma, Lei Zhu, Jiahui Wen, Mingyang Zhong

引用次数: 0

Low-Light Image Enhancement with Color Transfer Based on Local Statistical Feature 基于局部统计特征的色彩转移微光图像增强

International Conference on Advanced Data Mining and Applications Pub Date : 2020-11-12 DOI: 10.1007/978-3-030-65390-3_48

Zhigao Zhang, Bin Wang

引用次数: 0

MSPP: A Highly Efficient and Scalable Algorithm for Mining Similar Pairs of Points MSPP:一种高效、可扩展的相似点对挖掘算法

International Conference on Advanced Data Mining and Applications Pub Date : 2020-07-31 DOI: 10.1007/978-3-030-65390-3_3

S. Saha, A. Soliman, S. Rajasekaran

引用次数: 0

Single Image Dehazing Algorithm Based on Sky Region Segmentation 基于天空区域分割的单幅图像去雾算法

International Conference on Advanced Data Mining and Applications Pub Date : 2020-07-10 DOI: 10.1007/978-3-030-35231-8_35

Wei-xiang Li, Wei Jie, S. M. Zadeh

引用次数: 0