The International FLAIRS Conference Proceedings最新文献_第5页

Code Generation for Collectible Card Games with Complex APIs 具有复杂api的卡片收集游戏的代码生成

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133044

John Licato, Logan Fields, Brayden Hollis

引用次数: 0

Towards a Temporal Probabilistic Argumentation Framework 一个时间概率论证框架

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133267

Stefano Bistarelli, Victor David, Francesco Santini, Carlo Taticchi

引用次数: 1

Learning Sensor Based Risk Map Augmentation for Risk Aware UAS Operation 基于学习传感器的风险地图增强风险感知无人机操作

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133323

Bhaskar Trivedi, M. Huber

{"title":"Learning Sensor Based Risk Map Augmentation for Risk Aware UAS Operation","authors":"Bhaskar Trivedi, M. Huber","doi":"10.32473/flairs.36.133323","DOIUrl":"https://doi.org/10.32473/flairs.36.133323","url":null,"abstract":"Unmanned Aerial Systems (UAS) have become increasingly popular and have been identified as a good platform for a range of tasks from surveillance and inspection to delivery and maintenance. In many of these applications these systems have to operate in environments that are frequented by people or that contain sensitive infrastructure and in which the operation of UAS thus poses physical risk in terms of damage in case of vehicle failure or psychological or privacy risks which would make their operation less acceptable. To increase the use of these systems it is thus important that they can take into account these risks when determining navigation strategies. While this can sometimes be done based on prior information, such as street and building plans in cities, a priori information is often not complete, making it essential that risk representations can be augmented in real time based on sensor information. This paper presents an approach to risk map augmentation that uses learned risk identification from aerial pictures to fuse additional information with prior data into a dynamically changing risk map that allows effective re-planning of navigation strategies.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"337 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133870819","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

k-medianoids Clustering Algorithm k-medianoids聚类算法

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133379

James Cha, Teryn Cha, Sung-Hyuk Cha

引用次数: 0

Semi-supervised Learning of Visual Causal Macrovariables 视觉因果宏变量的半监督学习

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133229

Aruna Jammalamadaka, Lingyi Zhang, Joseph Comer, Sasha Strelnikoff, R. Mustari, Tsai-Ching Lu, Rajan Bhattacharyya

引用次数: 0

A Comparison of AutoML Hyperparameter Optimization Tools For Tabular Data 表格数据的AutoML超参数优化工具比较

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133357

Prativa Pokhrel, A. Lazar

{"title":"A Comparison of AutoML Hyperparameter Optimization Tools For Tabular Data","authors":"Prativa Pokhrel, A. Lazar","doi":"10.32473/flairs.36.133357","DOIUrl":"https://doi.org/10.32473/flairs.36.133357","url":null,"abstract":"The performance of machine learning (ML) methods for classification and regression tasks applied to tabular datasets is sensitive to hyperparameters values. Therefore, finding the optimal values of these hyperparameters is integral in improving the prediction accuracy of an ML algorithm and the model selection. However, manually searching for the best configuration is a tedious task, and many AutoML (Automated Machine Learning) frameworks have been proposed recently to help practitioners solve this problem. Hyperparameters are the values or configurations that control the algorithm’s behavior while building the model. Hyperparameter optimization (HPO) is the guided process of finding the best combination of hyperparameters that delivers the best performance on the data and task at hand in a reasonable amount of time. In this work, we compare the performance of two frequently used AutoML HPO frameworks, Optuna and HyperOpt, on popular OpenML tabular datasets to identify the best framework for tabular data. The results of the experiments show that Optuna performs better than HyperOpt, whereas HyperOpt is the fastest for hyperparameter optimization.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129206218","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

Aggregating Procedure for Fuzzy Cognitive Maps 模糊认知地图的聚合程序

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133082

Maikel Leon Espinosa

引用次数: 0

Using Bidirectional Associative Memory Neural Networks to Solve the N-bit Task 利用双向联想记忆神经网络解决n位任务

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133140

Damiem Rolon-Mérette, Thadde Rolon-Merette, S. Chartier

{"title":"Using Bidirectional Associative Memory Neural Networks to Solve the N-bit Task","authors":"Damiem Rolon-Mérette, Thadde Rolon-Merette, S. Chartier","doi":"10.32473/flairs.36.133140","DOIUrl":"https://doi.org/10.32473/flairs.36.133140","url":null,"abstract":"Nowadays, artificial neural networks can easily solve the N-bit parity problem. However, each time a different level must be learned, the network must be retrained. This, combined with the exponential increase of learning trials required as N grows, make these models too different from how their biological counterpart solves them. This is because humans learn to recognize patterns, count, and determine if numbers are odd or even. Once they have learned these tasks, they can have them interact to solve any level without further training. This behavior is akin to performing multiple associations of different tasks. Therefore, it is proposed that by using bidirectional associative memory neural networks, it would be possible to solve the N-bit parity problem in a similar fashion to humans. To achieve this, two networks interacted; one served as a task Identifier and the other as a memory Extractor, giving the desired behavior influenced by the Identifier. Results showed that the model could solve the 2- to 9-bit in linear time once the associations were learned. Moreover, this was possible with 97% fewer inputs and no retraining. In addition, because of the recurrent nature of the model, it could also solve the tasks even under high noise levels.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"176 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114091499","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

MarshCover: A Web-based Tool for Estimating Vegetation Coverage in Marsh Images Using Convolutional Neural Networks MarshCover:一个基于网络的工具，用于使用卷积神经网络估计沼泽图像中的植被覆盖

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133166

Lucas Welch, Xudong Liu

{"title":"MarshCover: A Web-based Tool for Estimating Vegetation Coverage in Marsh Images Using Convolutional Neural Networks","authors":"Lucas Welch, Xudong Liu","doi":"10.32473/flairs.36.133166","DOIUrl":"https://doi.org/10.32473/flairs.36.133166","url":null,"abstract":"Marsh ecosystems are some of our most important, serving many crucial ecological functions. They are also rapidly changing, and it is vital for scientists to track these changes. This includes monitoring the health of marshes via estimating ground coverage by various grass species, a task that requires human labor to look at marsh images and manually estimate the coverage. Clearly, this task can be quite formidable. To automate this standard yet laborsome process, we developa web-based system, called MarshCover, that automates the process of estimating vegetation density in marsh images using convolutional neural networks (CNNs). MarshCover, to the best of our knowledge, is the first such tool available to biologists that uses CNNs for marsh vegetation estimations. In order to select effective CNN models for our MarshCover server, we conduct extensive empirical analyses of three distinct CNNs, i.e., LeNet-5, AlexNet and VGG-16, to compare their performances on a public marsh image dataset. To this end, we address two classification problems for this paper: a binary classification problem classifying points as vegetated and unvegetated, and a multiclass classification problem that classifies points into either an unvegetated class or one of five different species classes. Our experiments identify the VGG16 model as the best classifier to embed in MarshCover for both the binary classification problem and the full classification problem with a two model classifier (called two-shot). These two classifiers had accuracies on test data of 90.76% and 84% respectively. MarshCover is publicly available online.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127325184","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

Automatic Summarization for Academic Articles using Deep Learning and Reinforcement Learning with Viewpoints 基于深度学习和强化学习的学术文章自动摘要

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133308

Jinghong Li, Hatsuhiko Tanabe, Koichi Ota, Wen Gu, S. Hasegawa

引用次数: 0