The International FLAIRS Conference Proceedings最新文献_第8页

Making Time Series Embeddings More Interpretable in Deep Learning - Extracting Higher-Level Features via Symbolic Approximation Representations 在深度学习中使时间序列嵌入更具可解释性——通过符号逼近表示提取高级特征

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133107

Leonid Schwenke, Martin Atzmueller

引用次数: 1

Towards a Framework for Intelligent Urban Traffic Routing 智慧城市交通路径框架研究

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133321

Matyáš Švadlenka, L. Chrpa

引用次数: 2

Enforcing Grammar in Code Synthesis with Transformers

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133363

Dmytro Vitel, Stephen Steinle, John Licato

引用次数: 0

Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach 基于知识图的生物医学语义标注方法

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133253

Asim Abbas, Mutahira Khalid, Sebastian Chalarca, Fazel Keshtkar, S. Bukhari

{"title":"Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach","authors":"Asim Abbas, Mutahira Khalid, Sebastian Chalarca, Fazel Keshtkar, S. Bukhari","doi":"10.32473/flairs.36.133253","DOIUrl":"https://doi.org/10.32473/flairs.36.133253","url":null,"abstract":"An abundance of biomedical data is generated in the form of clinical notes, reports, and research articles available online. This data holds valuable information that requires extraction, retrieval, and transformation into actionable knowledge. However, this information has various access challenges due to the need for precise machine-interpretable semantic metadata required by search engines. Despite search engines' efforts to interpret the semantics information, they still struggle to index, search, and retrieve relevant information accurately. To address these challenges, we propose a novel graph-based semantic knowledge-sharing approach to enhance the quality of biomedical semantic annotation by engaging biomedical domain experts. In this approach, entities in the knowledge-sharing environment are interlinked and play critical roles. Authorial queries can be posted on the \"Knowledge Cafe,\" and community experts can provide recommendations for semantic annotations. The community can further validate and evaluate the expert responses through a voting scheme resulting in a transformed \"Knowledge Cafe\" environment that functions as a knowledge graph with semantically linked entities. We evaluated the proposed approach through a series of scenarios, resulting in precision, recall, F1-score, and accuracy assessment matrices. Our results showed an acceptable level of accuracy at approximately 90%. The source code for \"Semantically\" is freely available at: https://github.com/bukharilab/Semantically\u0000 \u0000\u0000\u0000 \u0000 \u0000 \u0000\u0000","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"165 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":"122957038","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

Conditionals, Infeasible Worlds, and Reasoning with System W 条件、不可行的世界和系统W的推理

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133268

J. Haldimann, C. Beierle, G. Kern-Isberner, T. Meyer

{"title":"Conditionals, Infeasible Worlds, and Reasoning with System W","authors":"J. Haldimann, C. Beierle, G. Kern-Isberner, T. Meyer","doi":"10.32473/flairs.36.133268","DOIUrl":"https://doi.org/10.32473/flairs.36.133268","url":null,"abstract":"The recently introduced notion of an inductive inference operator captures the process of completing a given conditional belief base to an inference relation. System W is such an inductive inference operator exhibiting some notable properties like extending rational closure and satisfying syntax splitting for inference from conditional belief bases. However, the definition of system W and the shown results regarding its properties only take belief bases into account that satisfy a strong notion of consistency where no worlds may be completely infeasible. In this paper, we lift this limitation and extend the definition of system W to also cover belief bases that force some worlds to be infeasible. We establish the position of the extended system W within a map of other inductive inference operators being able to deal with the presence of infeasible worlds, including system Z and multipreference closure. For placing lexicographic inference in this map, we show that the definition of lexicographic inference must be slightly modified so that it is an inductive inference operator satisfying direct inference even when there are worlds that are infeasible. Furthermore, we show that, like its unextended version, the extended system W enjoys other desirable properties such as still fully complying with syntax splitting.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"111 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":"115946258","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

Learning Policies for Neural Network Architecture Optimization Using Reinforcement Learning 基于强化学习的神经网络架构优化学习策略

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133380

Raghav Vadhera, M. Huber

{"title":"Learning Policies for Neural Network Architecture Optimization Using Reinforcement Learning","authors":"Raghav Vadhera, M. Huber","doi":"10.32473/flairs.36.133380","DOIUrl":"https://doi.org/10.32473/flairs.36.133380","url":null,"abstract":"Deep learning systems tend to be very sensitive to the specific network architecture both in terms of learning ability and performance of the learned solution. This, together with the difficulty of tuning neural network architectures leads to a need for automatic network optimization. Previous work largely optimizes a network for one specific problem using architecture search, requiring significant amounts of time training different architectures during optimization. To address this and to open up the potential for transfer across tasks, this paper presents a novel approach that uses Reinforcement Learning to learn a policy for network optimization in a derived architecture embedding space that incrementally optimizes the network for the given problem. By utilizing policy learning and an abstract problem embedding, this approach brings the promise of transfer of the policy across problems and thus the potential optimization of networks for new problems without the need for excessive additional training. For an initial evaluation of the base capabilities, experiments for a standard classification problem are performed in this paper, showing the ability of the approach to optimize the architecture for a specific problem within a given rang of fully connected networks, and indicating its potential for learning effective policies to automatically improve network architectures.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"45 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":"127181910","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

Comparative Study Between Vision Transformer and EfficientNet on Marsh Grass Classification Vision Transformer与EfficientNet在沼泽草分类中的比较研究

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133132

Conrad Testagrose, Mehlam Shabbir, Braden Weaver, Xudong Liu

引用次数: 0

BERTimbau in Action: An Investigation of its Abilities in Sentiment Analysis, Aspect Extraction, Hate Speech Detection, and Irony Detection BERTimbau在行动中的应用:情感分析、方面提取、仇恨言论检测和反语检测能力的研究

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133186

Julia da Rocha Junqueira, F. Silva, Wesley Costa, Rodrigo Carvalho, A. Bender, U. Corrêa, L. Freitas

{"title":"BERTimbau in Action: An Investigation of its Abilities in Sentiment Analysis, Aspect Extraction, Hate Speech Detection, and Irony Detection","authors":"Julia da Rocha Junqueira, F. Silva, Wesley Costa, Rodrigo Carvalho, A. Bender, U. Corrêa, L. Freitas","doi":"10.32473/flairs.36.133186","DOIUrl":"https://doi.org/10.32473/flairs.36.133186","url":null,"abstract":"Social Media has revolutionized how individuals, groups, and communities interact. This immense quantity of unstructured data holds valuable information expressed in informal language. However, automatically extracting this information using Natural Language Processing requires adaptations of traditional methods or the development of new strategies capable of extracting information tackling web-prone language. BERT, a Deep Learning methodology proposed by Google in 2018, brought transfer learning to Natural Language Processing. In this work, we used a BERT model for the Portuguese language called BERTimbau to create models for Sentiment Analysis, Aspect Extraction, Hate Speech Detection, and Irony Detection. We experimented with the two BERTimbau models, base and large. Finally, we compared the results obtained in each task. Experiments with BERTimbau based models obtained improved results, F-Measure of 0.88 and 0.89 in Sentiment Analysis and Hate Speech Detection tasks, respectively, compared to classical Machine Learning approaches.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"1 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":"130546407","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

Evaluating Fairness in Predictive Policing Using Domain Knowledge 基于领域知识的预测警务公平性评价

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133088

Ava Downey, Sheikh Rabiul Islam, Md Kamruzzaman Sarker

引用次数: 0

SBERTiment: A New Pipeline to Solve Aspect Based Sentiment Analysis in the Zero-Shot Setting 一种解决零镜头环境下基于方面的情感分析的新方法

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133058

Matteo Muffo, A. Cocco, E. Negri, Enrico Bertino, Devi Veena Sreekumar, G. Pennesi, Riccardo Lorenzon

引用次数: 0