The International FLAIRS Conference Proceedings最新文献

Teaching Artificial Intelligence (AI) with AI for AI applications 教学人工智能(AI)与人工智能应用

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133388

K. Brawner, Ning Wang, Ben Nye

引用次数: 0

CyberReco: ~Cybersecurity Workforce Readiness Recommender System CyberReco: ~网络安全劳动力准备推荐系统

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133305

Ramoni O. Lasisi

引用次数: 0

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133040

Johannes Pauli, Maximilian Hoffmann, R. Bergmann

{"title":"Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning Using Graph Neural Networks and Transfer Learning","authors":"Johannes Pauli, Maximilian Hoffmann, R. Bergmann","doi":"10.32473/flairs.36.133040","DOIUrl":"https://doi.org/10.32473/flairs.36.133040","url":null,"abstract":"Similarity-based retrieval of semantic graphs is a crucial task of Process-Oriented Case-Based Reasoning (POCBR) that is usually complex and time-consuming, as it requires some kind of inexact graph matching. Previous work tackles this problem by using Graph Neural Networks (GNNs) to learn pairwise graph similarities. In this paper, we present a novel approach that improves on the GNN-based case retrieval with a Transfer Learning (TL) setup, composed of two phases: First, the pretraining phase trains a model for assessing the similarities between graph nodes and edges and their semantic annotations. Second, the pretrained model is then integrated into the GNN model by either using fine-tuning, i.e., the parameters of the pretrained model are further trained, or feature extraction, i.e., the parameters of the pretrained model are converted to constants. The experimental evaluation examines the quality and performance of the models based on TL compared to the GNN models from previous work for three semantic graph domains with various properties. The results show the great potential of the proposed approach for reducing the similarity prediction error and the training time.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"168 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":"115178582","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

Comparing Statistical Models for Retrieval based Question-answering Dialogue: BERT vs Relevance Models 基于检索的问答对话统计模型的比较:BERT与关联模型

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133386

Debaditya Pal, A. Leuski, D. Traum

引用次数: 1

On Bounding the Behavior of a Neuron 关于约束神经元的行为

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133336

Richard Borowski, Arthur Choi

引用次数: 0

Semantic Segmentation with Multispectral Satellite Images of Waterfowl Habitat 水禽栖息地多光谱卫星图像的语义分割

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133331

Mateo Gannod, Nicholas M. Masto, Collins Owusu, Cory J. Highway, Katherine E. Brown, Abigail G. Blake‐Bradshaw, Jamie C. Feddersen, H. Hagy, Douglas A. Talbert, Bradley Cohen

{"title":"Semantic Segmentation with Multispectral Satellite Images of Waterfowl Habitat","authors":"Mateo Gannod, Nicholas M. Masto, Collins Owusu, Cory J. Highway, Katherine E. Brown, Abigail G. Blake‐Bradshaw, Jamie C. Feddersen, H. Hagy, Douglas A. Talbert, Bradley Cohen","doi":"10.32473/flairs.36.133331","DOIUrl":"https://doi.org/10.32473/flairs.36.133331","url":null,"abstract":"\u0000 \u0000 \u0000Migratory waterfowl (i.e., ducks, geese, and swans) management relies on landscape bioenergetic models to inform on-the-ground habitat conditions and conservation practices. Therefore, conservation planners rely on accurate predictions of wetland habitats for waterfowl at regional scales. Unharvested flooded corn is a popular management tool on public and private lands that greatly increases landscape-level energy compared to other wetlands; thus, landscape bioenergetic models are particularly sensitive to these habitat features. Despite their importance to conservation planning and implementation, the abundance and distribution of unharvested flooded corn fields across North America is unknown. Furthermore, training data is difficult to collect and accurate predictions are challenging given their unique attributes and discreteness at landscape-level lens. Advances in multispectral imagery and deep learning algorithms may enable continuous and autonomous detection of these habitat features. Therefore, we conducted modeling experiments using training data of unharvested flooded corn fields in West Tennessee and multispectral imagery collected from Sentinel-2 satellite missions. We performed several experiments using individual band combination composites and/or vegetation indices to identify optimal bands using MRUNET architectures. We subsequently used 3 ensemble models of important individual networks. We found the use of multispectral bands was necessary and although the CIR composite and OSAVI index improved precision, the 12-band composite increased recall, the metric we were most interested in. Moreover, all ensembles exhibited poor performance. Here, we present results of our initial modeling experiments and suggest future modeling exercises including temporal image and vegetation index stacking using multi-modal and/or recurrent neural network architectures. \u0000 \u0000 \u0000","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"93 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":"124233568","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

A Q-Learning Proposal for Tuning Genetic Algorithms in Flexible Job Shop Scheduling Problems 柔性作业车间调度问题中遗传算法调优的q -学习方法

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133366

Christian Pérez, Carlos March, M. Salido

{"title":"A Q-Learning Proposal for Tuning Genetic Algorithms in Flexible Job Shop Scheduling Problems","authors":"Christian Pérez, Carlos March, M. Salido","doi":"10.32473/flairs.36.133366","DOIUrl":"https://doi.org/10.32473/flairs.36.133366","url":null,"abstract":"Genetic algorithms (GAs) belong to the category of evolutionary algorithms and are frequently utilized for resolving challenging combinatorial problems. However, they typically require customization to suit a particular problem type, and their performance is heavily influenced by numerous hyperparameters and reproduction operators. In this work, we propose a Reinforcement Learning approach for fine-tuning Genetic Algorithms in Flexible Job Shop Scheduling problems (FJSP), where the main parameters involved in the genetic algorithm operators are trained to allocate the most promising values. The approach returns an optimized schedule taking into account given constraints specific to the scenario, such as the relationship among release date, due date, and processing time, which machines must be selected out of a set of alternative machines, or which sequence-dependent setup time can be filtered. \u0000The approach takes input data in the form of FJSP instances by varying the numbers of jobs and machines and then uses the NSGA-II algorithm to generate solutions. These solutions are stored in a Solutions module and they are analyzed using a Principal components analysis (PCA) to identify clusters of similar instances and solutions. The Q-Learning module then generates hyperparameters for each iteration of the NSGA-II algorithm based on information from the previous modules. A toy example is presented to better understand the behavior of the proposal and the results obtained for optimizing further instances of the problem in a more efficient way.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"22 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":"126557099","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

A Comparative Study of Imputation Methods for Time Series Data 时间序列数据归算方法的比较研究

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133068

D. Khan, A. Lazar

引用次数: 0

SelfCode: An Annotated Corpus and a Model for Automated Assessment of Self-Explanation During Source Code Comprehension SelfCode:一个注释语料库和源代码理解过程中自我解释的自动评估模型

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133385

Jeevan Chapagain, Zak Risha, Rabin Banjade, P. Oli, L. Tamang, Peter Brusilovsky, V. Rus

{"title":"SelfCode: An Annotated Corpus and a Model for Automated Assessment of Self-Explanation During Source Code Comprehension","authors":"Jeevan Chapagain, Zak Risha, Rabin Banjade, P. Oli, L. Tamang, Peter Brusilovsky, V. Rus","doi":"10.32473/flairs.36.133385","DOIUrl":"https://doi.org/10.32473/flairs.36.133385","url":null,"abstract":"The ability to automatically assess learners' activities is the key to user modeling and personalization in adaptive educational systems.The work presented in this paper opens an opportunity to expand the scope of automated assessment from traditional programming problems to code comprehension tasks where students are requested to explain the critical steps of a program. The ability to automatically assess these self-explanations offers a unique opportunity to understand the current state of student knowledge, recognize possible misconceptions, and provide feedback. Annotated datasets are needed to train Artificial Intelligence/Machine Learning approaches for the automated assessment of student explanations. To answer this need, we present a novel corpus called SelfCode which consists of 1,770 sentence pairs of student and expert self-explanations of Java code examples, along with semantic similarity judgments provided by experts. We also present a baseline automated assessment model that relies on textual features. The corpus is available at the GitHub repository (https://github.com/jeevanchaps/SelfCode).","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"55 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":"125767134","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

Improving Word Embedding Using Variational Dropout 利用变分Dropout改进词嵌入

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133326

Zainab Albujasim, D. Inkpen, Xuejun Han, Yuhong Guo

引用次数: 0