The International FLAIRS Conference Proceedings最新文献

{"title":"Multi-hop Question Generation without Supporting Fact Information","authors":"John Emerson, Yllias Chali","doi":"10.32473/flairs.36.133320","DOIUrl":"https://doi.org/10.32473/flairs.36.133320","url":null,"abstract":"Question generation is the parallel task of question answering, where given an input context and optionally, an answer, the goal is to generate a relevant and fluent natural language question. Although recent works on question generation have experienced success by utilizing sequence-to-sequence models, there is a need for question generation models to handle increasingly complex input contexts with the goal of producing increasingly elaborate questions. Multi-hop question generation is a more challenging task that aims to generate questions by connecting multiple facts from multiple input contexts. In this work we apply a transformer model to the task of multi-hop question generation, without utilizing any sentence-level supporting fact information. We utilize concepts that have proven effective in single-hop question generation, including a copy mechanism and placeholder tokens. We evaluate our model's performance on the HotpotQA dataset using automated evaluation metrics and human evaluation, and show an improvement over the previous works. \u0000 ","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"46 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":"133592759","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}

{"title":"Generating more Realistic Packet Loss Patterns for Wireless links using Neural Networks","authors":"Daniel Otten, T. Hänel, Tim Römer, N. Aschenbruck","doi":"10.32473/flairs.36.133099","DOIUrl":"https://doi.org/10.32473/flairs.36.133099","url":null,"abstract":"Simulations of wireless network connections are essential forthe development of new technologies because they are farmore scalable than real-world experiments and reproducible.Modeling packet loss realistically provides a highly abstractyet powerful tool for the simulation of wirelesses links. Typi-cally, simple statistical models or replaying of recorded tracesare used for the simulation. For a proper parametrization ofsimple statistical models, recorded traces are required, too.Both approaches have drawbacks: replaying traces is limitedto the length of the traces, a repetition may lead to unwantedeffects in the simulation. The statistical models solve this, butthe resulting packet loss patterns significantly differ from realones. In this paper, we propose using a neural network in-stead. It takes the same kind of input, i.e., a real-world trace,but it can generate longer traces with more realistic loss pat-terns. We share pre-trained neural networks for multiple linksin office and industry scenarios with the community for usein future research.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"365 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":"134119718","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}

{"title":"Biogeography-based optimization for feature selection","authors":"M. Gholami, Malek Mouhoub, S. Sadaoui","doi":"10.32473/flairs.36.133230","DOIUrl":"https://doi.org/10.32473/flairs.36.133230","url":null,"abstract":"Data clustering has many applications in medical sciences, banking, and data mining. K-means is the most popular data clustering algorithm due to its efficiency and simplicity of implementation. However, K-means has some limitations, which may affect its effectiveness, such as all the features having the same degree of importance. To address these limitations and improve K-means accuracy, we adopt the Biogeography-Based Optimization (BBO) algorithm to select the most relevant features of datasets. Our primary idea is to reduce the intra-cluster distance while increasing the distance between clusters.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"38 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":"131657981","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}

{"title":"Graph Neural Networks for Link Prediction","authors":"A. Lazar","doi":"10.32473/flairs.36.133375","DOIUrl":"https://doi.org/10.32473/flairs.36.133375","url":null,"abstract":"Graph Neural Networks (GNNs) belong to a class of deep learning methods that are specialized for extracting critical information and making accurate predictions on graph representations. Researchers have been striving to adapt neural networks to process graph data for over a decade. GNNs have found practical applications in various fields, including physics simulations, object detection, and recommendation systems. Predicting missing links in graphs is a crucial problem in various scientific fields because real-world graphs are frequently incompletely observed. This task, also known as link prediction, aims to predict the existence or absence of links in a graph. This tutorial is designed for researchers who have no prior experience with GNNs and will provide an overview of the link prediction task. In addition, we will discuss further reading, applications, and the most commonly used software packages and frameworks.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"10 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":"131740919","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}

{"title":"Ranking-Based Case Retrieval with Graph Neural Networks in Process-Oriented Case-Based Reasoning","authors":"Maximilian Hoffmann, R. Bergmann","doi":"10.32473/flairs.36.133039","DOIUrl":"https://doi.org/10.32473/flairs.36.133039","url":null,"abstract":"In Process-Oriented Case-Based Reasoning (POCBR), experiential knowledge from previous problem-solving situations is retrieved from a case base to be reused for upcoming problems. The task of retrieval is approached in previous work by using Graph Neural Networks (GNNs) to learn workflow similarities which are, in turn, used to find similar workflows w.r.t. a query workflow. This paper is motivated by the fact that these GNNs are mostly used for predicting the similarity between two workflows (query and case), while the retrieval in CBR is only concerned with the ranking of the most similar workflows from the case base w.r.t. the query. Thus, we propose a novel approach to extend the GNN-based workflow retrieval by a Learning-to-Rank (LTR) component where rankings instead of similarities between cases are predicted. The main contribution of this paper addresses the changes to the GNNs from previous work, such that their model architecture predicts pairwise preferences between cases w.r.t. a query and that they can be trained using labeled preference data. In order to transform these preferences into a case ranking, we also describe rank aggregation methods with different levels of computational complexity. The experimental evaluation compares different models for predicting similarities and rankings in case retrieval scenarios. The results indicate the potential of our ranking-based approach in significantly improving retrieval quality with only small impacts on the performance.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"12 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":"115230219","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}

{"title":"Learning to Take Cover with Navigation-Based Waypoints via Reinforcement Learning","authors":"Timothy Aris, Volkan Ustun, Rajay Kumar","doi":"10.32473/flairs.36.133348","DOIUrl":"https://doi.org/10.32473/flairs.36.133348","url":null,"abstract":"This paper presents a reinforcement learning model designed to learn how to take cover on geo-specific terrains, an essential behavior component for military training simulations. Training of the models is performed on the Rapid Integration and Development Environment (RIDE) leveraging the Unity ML-Agents framework. This work expands on previous work on raycast-based agents by increasing the number of enemies from one to three. We demonstrate an automated way of generating training and testing data within geo-specific terrains. We show that replacing the action space with a more abstracted, navmesh-based waypoint movement system can increase the generality and success rate of the models while providing similar results to our previous paper's results regarding retraining across terrains. We also comprehensively evaluate the differences between these and the previous models. Finally, we show that incorporating pixels into the model's input can increase performance at the cost of longer training times.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"52 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120911744","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}

{"title":"Using Knowledge Graph Embedding for Fault Detection - A Case Study in Electric Vehicle Parts Assembly","authors":"Ziad Kobti, Joseph El-Ghaname","doi":"10.32473/flairs.36.133373","DOIUrl":"https://doi.org/10.32473/flairs.36.133373","url":null,"abstract":"Automotive manufacturers are under stressful timelines as they shift their focus from internal combustion engines (ICE) to electric (EV) and hybrid-electric vehicles (HEV). The demand for this rapid change is crucial to meet a growing consumer market. New manufacturing challenges coupled with rapid change can lead to substantial safety risks for consumers as well as financial liability for automakers, especially when recalls happen. The resulting misplacement, misalignment, or defective assembly of any of the components or connectors can result in critical or even fatal outcomes for consumers. Recent findings reported by CNBC revealed that the shift to electric vehicles had cost automakers billions of dollars (Kolodny 2022). The cost of recalling an EV far outweighs that of an ICE. For instance, the Ford Kuga plug-in HEV had re-calls costs of about $19,000 per vehicle, in contrast to a typical ICE vehicle recall that averages around $500 per vehicle (Isidore and Vales-Dapena 2022). Furthermore, the EV recall rate has been higher. For instance, China’s EV recall rate was approximately 6.9% of its total sales volume (Hao et al. 2021).Automakers are highly motivated to prevent automotive recalls by implementing and employing several preventative measures. IoT sensor-based fault detection systems, as well as those with camera capabilities, have been used to detect defects during production and assembly processes. Industry 4.0 standards (Garofalo et al. 2022) have been adopted, particularly when companies employ an autonomous assembly process.A critical issue in vision or sensor-based fault detection systems is their limitations, where they can only analyze and observe end components without analyzing the relationships and possible underlying connections with other components. For instance, these relationships can reveal whether a given component is missing or is connected correctly to another component. Simply relying on machine vision examining components in isolation, especially in uncontrolled manufacturing environments, becomes difficult and reliable, not to mention the extremely de-manding computational power needed for vision processing.The motivation of this research work is to present an alternative perspective that employs a collective view of components, represented as a networked graph, particularly a knowledge graph (KG) that we hypothesize its ability to be effective in analyzing data in the search for faults.KGs are a collection of real-world fact triplets of the structured form (head, relation, tail) (Hogan et al. 2022). Fundamentally, KGs can be expressed as a graph where nodes represent components or sub-components, and edges indicate a relationship between the two adjacent components. Hence, KG can be used to effectively represent and map interconnected components during and after manufacturing. Researchers have demonstrated the usefulness of Knowledge Graph Embedding (KGE) as a potential solution for automotive fault detection, and","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"107 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":"115900225","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}

{"title":"Topological Data Analysis in Natural Language Processing - A Tutorial","authors":"Wlodek Zadrozny","doi":"10.32473/flairs.36.133337","DOIUrl":"https://doi.org/10.32473/flairs.36.133337","url":null,"abstract":"Topological Data Analysis (TDA) introduces methods that capture the underlying structure of shapes in data.Within the last two decades, TDA has been mostly examined in unsupervised machine learning tasks. TDAhas been often considered an alternative to the conventional algorithms due to its capability to deal with highdimensionaldata. in different tasks including but not limited to clustering, This tutorial will focus on applications of topological data analysis to text data.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"27 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":"122365149","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}

{"title":"Towards Species-Specific Coral Classification in Reef Monitoring Efforts","authors":"H. Jang, J. Leidig, G. Wolffe","doi":"10.32473/flairs.36.133355","DOIUrl":"https://doi.org/10.32473/flairs.36.133355","url":null,"abstract":"\u0000Monitoring the health of coral reefs has traditionally required human labor-intensive effort in the collection and analysis of captured survey data and underwater images. Typical Marine Ecology tasks involve the classification of features within benthic maps and the estimation of coral coverage of the seafloor. In an effort to determine the feasibility of automating part of this process, this work trained and evaluated machine learning models to classify eleven species of stony and fire corals. A binary classifier was developed for each separate species (attaining 95-99% accuracy per model), followed by a single multi-class model (attaining over 92% accuracy). This paper details the architecture, parameterization, and effectiveness of these models as trained on a curated set of images. The models were then evaluated using one square kilometer maps of the seafloor to assess their practicability for automating several image-based analysis tasks on a widespread scale. Developing future monitoring workflows that utilize these machine learning models will minimize the human labor-intensive component of benthic map analysis. \u0000","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"52 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":"123324606","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}

{"title":"Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems","authors":"Aayush Singha Roy, Edoardo D'Amico, A. Lawlor, Neil Hurley","doi":"10.32473/flairs.36.133307","DOIUrl":"https://doi.org/10.32473/flairs.36.133307","url":null,"abstract":"Fashion industry is driven by fashion cycles, in which a fashion item is launched, rises to mainstream appeal and becomes a trend, then diminishes and eventually becomes obsolete. These properties make it critical to incorporate temporal information when adapting a recommendation framework to be employed in the fashion domain. However, an industry standard real-world recommendation architecture entails numerous phases, including data preparation, establishing and training recommender models, filtering and fulfilling revenue-based user needs. The contributions of the presented work are twofold. We first formalise the multi-stage recommendation pipeline by including the time dimension intrinsically present in the fashion data. We then present a study to incorporate explicit fashion domain characteristics into the presented pipeline. Finally, we conduct comprehensive experimentation on a real-world web-scale fashion dataset released by H&M, illustrating how including domain knowledge in the multi-stage framework can lead to significantly improvement on the final recommendation performance.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"29 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":"125144404","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}