2022 IEEE International Conference on Data Mining Workshops (ICDMW)最新文献_第10页

Diagonally Colorized iVAT Images for Labeled Data 标记数据的对角线彩色iVAT图像

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00043

Elizabeth D. Hathaway, R. Hathaway

引用次数: 0

A Comparison of Ambulance Redeployment Systems on Real-World Data 基于真实世界数据的救护车重新部署系统的比较

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00010

Niklas Strauß, Max Berrendorf, Tom Haider, M. Schubert

{"title":"A Comparison of Ambulance Redeployment Systems on Real-World Data","authors":"Niklas Strauß, Max Berrendorf, Tom Haider, M. Schubert","doi":"10.1109/ICDMW58026.2022.00010","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00010","url":null,"abstract":"Modern Emergency Medical Services (EMS) benefit from real-time sensor information in various ways as they provide up-to-date location information and help assess current local emergency risks. A critical part of EMS is dynamic ambulance redeployment, i.e., the task of assigning idle ambulances to base stations throughout a community. Although there has been a considerable effort on methods to optimize emergency response systems, a comparison of proposed methods is generally difficult as reported results are mostly based on artificial and proprietary test beds. In this paper, we present a benchmark simulation environment for dynamic ambulance redeployment based on real emergency data from the city of San Francisco. Our proposed simulation environment is highly scalable and is compatible with modern reinforcement learning frameworks. We provide a comparative study of several state-of-the-art methods for various metrics. Results indicate that even simple baseline algorithms can perform considerably well in close-to-realistic settings. The code of our simulator is openly available at https://github.com/niklasdbs/ambusim.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133739470","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: 1

Making Sense of Sentiments for Aesthetic Plastic Surgery 审美整形手术的情感理解

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00061

A. Choudhary, E. Cambria

{"title":"Making Sense of Sentiments for Aesthetic Plastic Surgery","authors":"A. Choudhary, E. Cambria","doi":"10.1109/ICDMW58026.2022.00061","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00061","url":null,"abstract":"With social media pervading all aspects of our life, the opinions expressed by netizens are a gold mine ready to be exploited in a meaningful way to influence all major public do-mains. Sentiment analysis is a way to interpret this unstructured data using AI tools. It is a well-known fact that there has been a 'Zoom Boom’ in the field of aesthetic plastic surgery due to the COVID-19 pandemic and the same has put the focus of attention sharply on our appearance. Polarity detection of tweets published on popular aesthetic plastic surgery procedures before and after the onset of COVID can provide great insights for aesthetic plastic surgeons and the health industry at large. In this work, we develop an end-to-end system for the sentiment analysis of such tweets incorporating a state-of-the-art fine-tuned deep learning model, an ingenious 'keyword search and filter approach’ and SenticNet. Our system was tested on a large database of 196,900 tweets and the results were visualized using affectively correct word clouds and also subjected to rigorous statistical hypothesis testing to draw meaningful inferences. The results showed a high level of statistical significance.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131449944","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

Large-Scale Sequential Utility Pattern Mining in Uncertain Environments 不确定环境下大规模顺序效用模式挖掘

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00077

J. Wu, Shuo Liu, Jerry Chun‐wei Lin

{"title":"Large-Scale Sequential Utility Pattern Mining in Uncertain Environments","authors":"J. Wu, Shuo Liu, Jerry Chun‐wei Lin","doi":"10.1109/ICDMW58026.2022.00077","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00077","url":null,"abstract":"High utility sequential pattern mining (HUSPM) considers timestamp, internal quantization, and external utility factors to mine high utility sequential patterns (HUSP), which has taken an essential place in data mining. The data collection may be uncertain in real life due to environmental factors, equipment limitations, privacy issues, etc. With the rapid increase of uncertain data volume, the efficiency of traditional mining algorithms decreases seriously. When the data volume is large, the conventional stand-alone algorithm will generate more candidate sequences, occupy a lot of memory, and significantly affect the execution speed. This paper designs a high utility probability sequence pattern mining algorithm based on MapReduce. The algorithm utilizes the MapReduce framework to solve the bottleneck of single-computer operation when the data volume is too large. The algorithm adopts an effective pruning strategy, which can effectively handle and reduce the number of candidate itemsets generated, thus the performance of the designed model can be greatly improved. The performance of the proposed algorithm is verified experimentally, and the correctness and completeness of the proposed algorithm are demonstrated and discussed to show the great achievement of the designed model.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132924428","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

Distributed LSTM-Learning from Differentially Private Label Proportions 基于不同自有标签比例的分布式lstm学习

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00139

Timon Sachweh, Daniel Boiar, T. Liebig

引用次数: 0

A study of automatic speech recognition in Portuguese by the Brazilian General Attorney of the Union 在葡萄牙语自动语音识别的研究由巴西总检察长联盟

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00038

Rodrigo Fay Verqara, Paulo Henrique dos Santos, Guilherme Fay Verqara, Fábio L. L. Mendonça, C. E. L. Veiga, B. Praciano, Daniel Alves da Silva, Rafael Timóteo de Sousa Júnior

引用次数: 1

Improving net ecosystem CO2 flux prediction using memory-based interpretable machine learning 利用基于记忆的可解释机器学习改进净生态系统二氧化碳通量预测

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00145

Siyan Liu, Dawei Lu, D. Ricciuto, A. Walker

{"title":"Improving net ecosystem CO2 flux prediction using memory-based interpretable machine learning","authors":"Siyan Liu, Dawei Lu, D. Ricciuto, A. Walker","doi":"10.1109/ICDMW58026.2022.00145","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00145","url":null,"abstract":"Terrestrial ecosystems play a central role in the global carbon cycle and affect climate change. However, our predictive understanding of these systems is still limited due to their complexity and uncertainty about how key drivers and their legacy effects influence carbon fluxes. Here, we propose an interpretable Long Short-Term Memory (iLSTM) network for predicting net ecosystem CO2 exchange (NEE) and interpreting the influence on the NEE prediction from environmental drivers and their memory effects. We consider five drivers and apply the method to three forest sites in the United States. Besides performing the prediction in each site, we also conduct transfer learning by using the iLSTM model trained in one site to predict at other sites. Results show that the iLSTM model produces good NEE predictions for all three sites and, more importantly, it provides reasonable interpretations on the input driver's importance as well as their temporal importance on the NEE prediction. Additionally, the iLSTM model demonstrates good across-site transferability in terms of both prediction accuracy and interpretability. The transferability can improve the NEE prediction in unobserved forest sites, and the interpretability advances our predictive understanding and guides process-based model development.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126631298","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

Deep Transfer Tensor Factorization for Multi-View Learning 多视图学习的深度传递张量分解

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00067

Penghao Jiang, Ke Xin, Chunxi Li

引用次数: 0

AARS: A novel adaptive archive-based efficient counting method for machine learning applications AARS:一种用于机器学习应用的基于档案的新型自适应高效计数方法

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00085

Sajib K. Biswas, Pranab K. Muhuri, Uttam K. Roy

引用次数: 0

Using spatial data and cluster analysis to automatically detect non-trivial relationships between environmental transgressors 利用空间数据和聚类分析自动检测环境违规者之间的非琐碎关系

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00022

José Alberto Sousa Torres, Paulo Henrique dos Santos, Daniel Alves da Silva, C. E. L. Veiga, Márcio Bastos Medeiros, Guilherme Fay Verqara, Fábio L. L. Mendonça, Rafael Timóteo de Sousa Júnior

{"title":"Using spatial data and cluster analysis to automatically detect non-trivial relationships between environmental transgressors","authors":"José Alberto Sousa Torres, Paulo Henrique dos Santos, Daniel Alves da Silva, C. E. L. Veiga, Márcio Bastos Medeiros, Guilherme Fay Verqara, Fábio L. L. Mendonça, Rafael Timóteo de Sousa Júnior","doi":"10.1109/ICDMW58026.2022.00022","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00022","url":null,"abstract":"The Amazon Rainforest is the most significant biodiversi-ty reserve on the planet. It plays a central role in combating global warming and climate change on the Earth. De-spite its importance, in 2021, the illegal deforestation process in the Brazilian Amazon rainforest had the worst year in a decade. The data show that more than 10,000 kilometers of native forest were destroyed that year-an increase of 29% compared to 2020. To fight against the action of deforesters, Brazilian environmental inspection agencies imposed more than 14 billion dollars in environmental fines in recent decades. However, it has not effectively reduced deforestation as only 4% of this amount was effectively collected-not inhibiting lawbreakers from deforesting. This is due to the difficulty of identifying the real transgressors, who use scapegoats to hide their crimes. The main objective of this paper is to propose an approach to find the real environmental transgressors through the analysis of data related to the fines imposed by Brazilian governmental agencies in the last three decades. We propose a method that employ clustering techniques in geo-graphic and temporal data extracted from fines to identify non-trivial correlations between scapegoats and large landowners. The automatically identified links were load-ed into a graph analysis database for accuracy assessment. The observed results were positive and indicated that this strategy could effectively identify the real culprits.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128219766","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