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检索条件"丛书名=Communications in Computer and Information Science"

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Adaptation Dynamics of Galapagos Finches: Evolutionary Responses to Climate Variation Explored through Machine Learning 12th

Adaptation Dynamics of Galapagos Finches: Evolutionary Respo...

引用

12th Ecuadorian Conference on information and Communication Technologies

作者： Jimenez Narvaez, Ariana Deyaneira Sanchez Vera, Danely Leonor Reyes, Ivan Vizcaino-Imacana, Paulina Morocho-Cayamcela, Manuel Eugenio Yachay Tech Univ Sch Math & Computat Sci Urcuqui 100119 Ecuador Univ Internac Ecuador Sch Comp Sci Fac Tech Sci Quito 170411 Ecuador

ISBN: (纸本)9783031754302;9783031754319

Climate change is accelerating the evolution of species as global warming intensifies and natural environments undergo significant alterations. This study addresses the urgent need to understand how these environmental changes impact avian populations, specifically focusing on the physiological adaptation of finch beaks on the Galapagos Islands. By employing multiple models-logistic regression, XGBOOST, and Random Forest Classifier-using the most recent data, the study investigates how changes in climate conditions correlate with variations in beak dimensions. The analysis reveals significant relationships between temperature, precipitation, and finch beak characteristics, indicating that climatic factors have a notable impact on beak morphology. The study aims to clarify the evolutionary dynamics at play and to predict future adaptations based on projected climate scenarios. The results highlight the robust correlation between climatic variables and finch physiology, providing deeper insights into the complex interplay between climate change and evolutionary biology. These findings are crucial for developing targeted conservation and adaptation strategies, helping to safeguard biodiversity in the face of ongoing climate shifts. The insights gained from this study are essential for guiding policymakers and stakeholders in implementing effective measures to mitigate the impacts of climate change on ecosystems and species survival.

关键词： Evolutionary adaptation Predictive modeling Data analysis Climate change Artificial intelligence Biodiversity Conservation

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A Continuous Structural Intervention Distance to Compare Causal Graphs 6th

A Continuous Structural Intervention Distance to Compare Cau...

引用

6th Pacific Causal Inference Conference

作者： Dhanakshirur, Mihir Laumann, Felix Park, Junhyung Barahona, Mauricio Indian Inst Sci Bengaluru Dept Math Bangalore Karnataka India Imperial Coll London Dept Math London England MPI Intelligent Syst Tubingen Germany

ISBN: (纸本)9789819778119;9789819778126

Causal inference under interventions requires accurate assessment of differences between true and learned causal graphs. We introduce a new continuous metric that extends beyond graph-based measures like Structural Hamming Distance and Structural Intervention Distance by incorporating underlying data alongside graph structures. Our approach embeds intervention distributions for each node pair as conditional mean embeddings in reproducing kernel Hilbert spaces, then quantifies their disparity using maximum (conditional) mean discrepancy. We present theoretical findings supported by synthetic data experiments.

关键词： Directed acyclic graphs graph distance conditional mean embeddings kernel methods

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Multi-agent Path Planning for Logistics Cargo Environment Using LSTM Based Reinforcement Learning 5th

Multi-agent Path Planning for Logistics Cargo Environment Us...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Cho, Gun Rae Park, Sungho Jung, Eui-Jung Shin, Hyunseok Son, So Eun Choi, Yong Korea Inst Robot & Technol Convergence Pohang 37666 South Korea

ISBN: (纸本)9783031807596;9783031807602

In the operation of logistics cargo, temporal efficiency in handling logistics is a critical issue. One solution to enhance this efficiency is the deployment of multiple autonomous ground vehicles (AGVs). This paper proposes a reinforcement learning approach based on Long Short-Term Memory (LSTM) for multi-agent path planning in the logistics cargo environment. When AGVs are treated as moving obstacles to each other, the application of LSTM allows for path planning that aptly addresses changes over time within the environment. Additionally, to solve the well-known problem of the sparse reward in pathfinding, we propose a reinforcement learning architecture for multi-agent path planning that uses the path planning results of a single agent, guided by Q-learning, as the guide path. Furthermore, we have established state variables independent of the number of agents by setting interest window-based state variables, and introduced revisit rewards, effectively resolving the issue of local minima caused by repetitive movement-avoidance behavior between agents. Simulation results, emulating a real-world logistics warehouse environment, demonstrate the proposed technique's capability for effective multi-agent path planning in such settings.

关键词： Multi-agent path planning Reinforcement learning Long-short term memory Autonomous ground vehicles Logistics cargo

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Optimizing Vehicle Emissions: Identifying High-Impact Vehicles and Exploring Sustainable Fuel Options 4th

Optimizing Vehicle Emissions: Identifying High-Impact Vehicl...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Muttal, Channabasappa Sujatha, C. Pandekamat, Manoj Hiremath, Veeresh Pujari, Rahul KLE Technol Univ Hubli 580031 India

ISBN: (纸本)9783031837920;9783031837937

This study presents an extensive examination of emissions from passenger vehicles, particularly focusing on the benefits of hybrid vehicles over conventional vehicles powered by fossil fuels. Although hybrid vehicles have a more favorable environmental impact, their production is hindered by higher costs and production challenges. By utilizing a Linear Regression Model, this study confirms the positive correlation between fuel consumption and CO2 emissions, emphasizing that increased fuel consumption results in increased CO2 emissions. These findings highlight the need for policies that encourage the adoption and production of hybrid vehicles to mitigate vehicular pollution effectively. In addition, the study analyzed real-time vehicle emission data and employed predictive modeling to assess the influence of factors, including fuel type, driving habits, vehicle maintenance, and environmental conditions, on emissions. In addition, it compares laboratory-based emission tests with real-world measurements, revealing significant disparities and highlighting the importance of real-world emission monitoring. This study offers critical insights for policymaker and stakeholders working to develop sustainable transportation solution, and serves as a foundation for future strategies to address the environmental and health impacts of urban transportation. This study presents a linear regression model to predict vehicle CO2 emissions (g/km) from fuel consumption (L/100km) and vice versa, achieving 92.44% of accuracy for cars and 86.68% for vans. This tool facilitates comparisons of fuel consumption or CO2 emissions between cars and vans, helping policymakers and stakeholders craft sustainable transportation strategies.

关键词： Vehicular emissions Hybrid vehicles Fuel consumption CO2 emissions Predictive modeling Sustainable transportation

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Convolutional Neural Networks for the Classification of Comet Assay Cells 5th

Convolutional Neural Networks for the Classification of Come...

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5th International Symposium on Intelligent Computing Systems

作者： Rizo-Blanco, Ismel Martin-Gonzalez, Anabel Brito-Loeza, Carlos Legarda-Saenz, Ricardo Espinosa-Romero, Arturo Lafon-Hughes, Laura Univ Autonoma Yucatan Fac Matemat Computat Learning & Imaging Res Merida 97119 Mexico Univ Republ Ctr Univ Reg Litoral Norte CENUR Salto Uruguay

ISBN: (纸本)9783031829307;9783031829314

The comet assay is a versatile method used to determine the DNA damage in individual cells. The cells processed by this technique preserve the stable genetic material in the head of the comet and the unstable portion of DNA in the tail. The analysis of the resulting microscopic images from this test must be carried out by an expert, who must precisely determine the extent of DNA liberated from the head of the comet, since it is related to the level of damage. Since this is a time-consuming and very specialized task, the objective of this research is to develop a computational system, based on the use of a convolutional neural network, for the automatic classification of cells processed by comet assay according to the level of DNA damage they present.

关键词： Comet Assay Convolutional Neural Network Cell Classification Deep Learning

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RECE: A Family of Explainable Rubrics for Autograding Refute Problems 17th

RECE: A Family of Explainable Rubrics for Autograding Refute...

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17th India Compute Conference-COMPUTE-Annual

作者： Kumar, Viraj Indian Inst Sci Bangalore Bengaluru Karnataka India

ISBN: (纸本)9783031843907;9783031843914

A Refute problem provides students with (1) a programming task and (2) a buggy solution (typically, a function) for that task. A student answers a Refute problem by specifying an input on which the function does not return the expected result (as per the task). This paper makes three contributions. First, by identifying inconsistencies in scores to Refute problems produced manually by computer science faculty, we demonstrate that it is challenging to manually score such problems. Second, we identify a key category of near-correct responses whose importance seems to be under-appreciated by faculty in our study. Together, these findings indicate a need for rubrics that can support automated grading and are easy to explain. As a third contribution, we propose RECE (rhyming with peace), a family of simple rubrics for autograding Refute problems. RECE rubrics reward near-correct responses from the above category, but they otherwise align with the scores suggested by faculty. We believe that these contributions will aid faculty in introducing Refute problems as assessment items in programming courses.

关键词： Refute Problems Rubrics Autograding Programming

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Privacy Protection and Standardization of Electronic Medical Records Using Large Language Model

Privacy Protection and Standardization of Electronic Medical...

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2024 International Workshop on Deidentification of Electronic Medical Record Notes

作者： Huang, Chao-Long Rianto, Babam Sun, Jun-Teng Fu, Zheng-Xin Lee, Chung-Hong Natl Kaohsiung Univ Sci & Technol Kaohsiung 807618 Taiwan

ISBN: (纸本)9789819779659;9789819779666

Recently, the widespread application of electronic medical records (EMRs) has made protecting patients' personal privacy information crucial and highly important. However, the sources of these EMRs are different, resulting in various time formats, and this inconsistency makes historical data challenging to read. Therefore, the key to solving the problem lies in two major issues in medical information: de-identification of sensitive health information (SHI) as defined by the Health Insurance Portability and Accountability Act (HIPAA) and normalization of time according to the ISO 8601 standard. To identify the items in case reports, we used the powerful capabilities of large language models (LLMs) as the foundation. We developed a set of algorithms based on In Context Learning (ICL), which ensures that the model inference process is always in the correct order while allowing the features contained in invalid labels to be utilized. The efficacy of our method has been validated through its exceptional performance in a real-world competition, attaining an macro-F-measure of 93.51% on the de-identification task and remarkably achieving over 92% accuracy for time normalization.

关键词： De-identification Time Normalization Large Language Models Electronic Medical Records Sensitive Health information

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Design of IoT Architecture and LLM Model for Personalized Training Recommendations for Athletes 4th

Design of IoT Architecture and LLM Model for Personalized Tr...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Razo-Ballon, Hernan Ticona-Esquivel, Rodrigo Montalvo, Peter Univ Peruana Ciencias Aplicadas Lima Peru

ISBN: (纸本)9783031832093;9783031832109

We propose an architecture of Internet of Things (IoT) and a Large Language Model (LLM) to enhance the training and performance of athletes globally by creating personalized training routines based on their distance goals and physiological and environmental data. Originally motivated by the declining athletic performance at a national level, this work provides a comprehensive system that collects real-time physiological and environmental data through smartwatches, enabling personalized training guidance suitable for athletes worldwide, regardless of their discipline or proficiency level. Our main contribution is the design of an IoT architecture paired with an LLM model, specifically the Llama-2-7b model, which we fine-tuned with diverse training routines to offer tailored recommendations based on an athlete's specific distance goal. The seamless integration of IoT devices and LLM aims to enhance the overall training experience by leveraging real-time data for precise and adaptive training plans. Experimental results demonstrate the LLM's ability to generate daily, adaptive training plans that show promise in comparison to the non-tuned model. This integrative approach aims not only to revitalize athletics but also to provide a data-driven foundation for elevated athletic performance on a global scale.

关键词： Athlete Performance Enhancement IoT Large Language Models Fine-tuning Personalized Training Recommendations Smartwatch sensors Sports Data Analytics Athletic Training Optimization AI in Athletic Training Application for Athletes

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User Perception of Ontology-Based Explanations of AI Models 8th

User Perception of Ontology-Based Explanations of AI Models

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Agafonov, Anton Ponomarev, Andrew Smirnov, Alexander Russian Acad Sci St Petersburg Fed Res Ctr 14th Line 39 St Petersburg 199178 Russia

ISBN: (纸本)9783031838446;9783031838453

For using AI models in high-stake applications it is crucial for a decision maker to understand why the model came to a certain conclusion. Ontology-based explanation techniques of artificial neural networks aim to provide explanations adapted to domain vocabulary (which is encoded using ontology) in order to make them easier to interpret and reason about. However, few studies actually explore the perception of ontology-based explanations and their effectiveness with respect to more common explanation techniques for neural networks (e.g., LIME, GradCAM, etc.). The paper proposes two benchmark datasets with different task representations (tabular and graph) and a methodology to compare users' effectiveness of processing explanations, employing both objective (decision time, accuracy) and subjective metrics. The methodology and datasets were then used in a user study to compare several explanation representations (non-ontology-based and three ontology-based ones-textual representation of ontological inference, inference graph, and attributive). It was found that according to subjective evaluation, graph and textual explanations caused the least difficulty for the participants. Objective metrics vary with the size of the ontology, but inference graphs show good results in all the examined cases. Surprisingly, non-ontology-based explanations have almost the same positive effect on decision-making than ontology-based (although, a bit harder subjectively).

关键词： XAI Explainable AI Ontology Ontology-Based explanations User study Machine learning Neural networks

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Multiscale Convolutional Feature Aggregation for Fine-Grained Image Retrieval 7th

Multiscale Convolutional Feature Aggregation for Fine-Graine...

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8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Yang, Caolin Ding, Kaifeng Yang, Chengzhuan Zhejiang Normal Univ Jinhua 321004 Zhejiang Peoples R China

ISBN: (纸本)9789819600540;9789819600557

Fine-grained image retrieval aims to retrieve images from the same subclass as the query images from a database containing a specific class object. To solve the problem of large inter-class differences and small intra-class differences, we propose the Multiscale Convolutional Feature Aggregation (MsCFA) method accomplished in an unsupervised environment. This method comprises an object localization submodule and a multiscale feature fusion module. In the object localization module, we perform a coarse localization of the object based on the response levels within the feature map. Specifically, we crop the high-response regions of the feature map to ensure that the resulting cropped feature map contains more object features and less background noise. Subsequently, we employ the regional maximum activation of convolutions method to extract features from this cropped region. The cropped object feature is fused with the uncropped original feature to obtain the feature representation on a single scale. In the feature fusion module, we input images of different scales into the network to obtain feature representations at various scales and then fuse these different scale features to obtain our multiscale convolutional feature aggregation descriptor. In experiments on five classical fine-grained datasets, our method outperforms other fine-grained retrieval tasks in the same type of unsupervised environment and is comparable to those supervised tasks with end-to-end training.

关键词： Fine-grained Image Retrieval Convolutional Feature Feature Extraction Feature Fusion Multiscale Images

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