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Deep-learning-Based Precipitation Nowcasting with Ground Weather Station data and Radar data 22

Deep-Learning-Based Precipitation Nowcasting with Ground Wea...

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22nd IEEE international conference on data Mining (ICDM)

作者： Ko, Jihoon Lee, Kyuhan Hwang, Hyunjin Shin, Kijung Korea Adv Inst Sci & Technol Kim Jaechul Grad Sch AI Seoul South Korea

ISBN: (纸本)9798350346091

Recently, many deep-learning techniques have been applied to various weather-related prediction tasks, including precipitation nowcasting (i.e., predicting precipitation levels and locations in the near future). Most existing deep-learning-based approaches for precipitation nowcasting, however, consider only radar and/or satellite images as inputs, and meteorological observations collected from ground weather stations, which are sparsely located, are relatively unexplored. In this paper, we propose ASOC, a novel attentive method for effectively exploiting ground-based meteorological observations from multiple weather stations. ASOC is designed to capture temporal dynamics of the observations and also contextual relationships between them. ASOC is easily combined with existing image-based precipitation nowcasting models without changing their architectures. We show that such a combination improves the average critical success index (CSI) of predicting heavy (at least 10 mm/hr) and light (at least 1 mm/hr) rainfall events at 1-6 hr lead times by 5.7%, compared to the original image-based model, using the radar images and ground-based observations around South Korea collected from 2014 to 2020.

关键词： precipitation nowcasting ground-based meteorological observations attention mechanism with sparse features

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The Cybernetic Artificial Intelligence (CAI): Α new scientific field for modelling and controlling Complex Dynamical Systems 22

The Cybernetic Artificial Intelligence (CAI): Α new scienti...

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22nd IFAC conference on Technology, Culture and international Stability, TECIS 2024

作者： Groumpos, Peter P. Department of Electrical and Computer Engineering University of Patras Greece26550 Greece

Artificial Intelligence (AI) has been considered a revolutionary and world-changing science, although it is still a young field and has a long way to go before it can be established as a viable theory. Every day, new knowledge is created at an unthinkable speed, and the Big data Driven World is already upon us. AI has developed a wide range of theories and software tools that have shown remarkable success in addressing difficult and challenging societal problems. However, the field also faces many challenges and drawbacks that have led some people to view AI with skepticism. One of the main challenges facing AI is the difference between correlation and causation, which plays an important role in AI studies. Additionally, although the term Cybernetics should be a part of AI, it was ignored for many years in AI studies. To address these issues, the Cybernetic Artificial Intelligence (CAI) field has been proposed and analyzed here for the first time. The Fuzzy Cognitive Map approach is used for creating new knowledge. An algorithm for developing the CAI approach is proposed. The only hope for the survival of the planet lies in the quick development of Cybernetic Artificial Intelligence (CAI). The importance of differentiating between professional/personal ethics and eternal values is highlighted, and their importance in future AI applications is emphasized for solving challenging societal problems. Ultimately, the future of AI heavily depends on accepting certain ethical values and the acceptance of the CAI as a viable new scientific field. © 2024 The Authors. This is an open access article under the CC BY-NC-nd license.

关键词： Adversarial machine learning

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Automating Lichen Monitoring in Ecological Studies Using Instance Segmentation of Time-Lapse Images 22

Automating Lichen Monitoring in Ecological Studies Using Ins...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Naimi, Safwen Koubaa, Olfa Bouachir, Wassim Bilodeau, Guillaume-Alexandre Jeddore, Gregory Baines, Patricia Correia, David L. P. Arsenault, Andre Data Science Laboratory MontréalQC Canada Polytechnique Montréal Litiv Lab. MontréalQC Canada Conne River NL Canada Corner BrookNL Canada

ISBN: (纸本)9798350345346

Lichens are symbiotic organisms composed of fungi, algae, and/or cyanobacteria that thrive in a variety of environments. They play important roles in carbon and nitrogen cycling, and contribute directly and indirectly to biodiversity. Ecologists typically monitor lichens by using them as indicators to assess air quality and habitat conditions. In particular, epiphytic lichens, which live on trees, are key markers of air quality and environmental health. A new method of monitoring epiphytic lichens involves using time-lapse cameras to gather images of lichen populations. These cameras are used by ecologists in Newfoundland and Labrador to subsequently analyze and manually segment the images to determine lichen thalli condition and change. These methods are time-consuming and susceptible to observer bias. In this work, we aim to automate the monitoring of lichens over extended periods and to estimate their biomass and condition to facilitate the task of ecologists. To accomplish this, our proposed framework uses semantic segmentation with an effective training approach to automate monitoring and biomass estimation of epiphytic lichens on time-lapse images. We show that our method has the potential to significantly improve the accuracy and efficiency of lichen population monitoring, making it a valuable tool for forest ecologists and environmental scientists to evaluate the impact of climate change on Canada's forests. To the best of our knowledge, this is the first time that such an approach has been used to assist ecologists in monitoring and analyzing epiphytic lichens. © 2023 IEEE.

关键词： Climate Change Epiphytic Lichens Forest Ecology Instance Segmentation Mask Scoring R-CNN

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Fine-Tuned BERT Model for Hate Speech Detection in Political Discourse 22

Fine-Tuned BERT Model for Hate Speech Detection in Political...

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22nd international conference on ICT and Knowledge engineering, ICT and KE 2024

作者： Nwaiwu, Steve Jongsawat, Nipat Tungkasthan, Anucha Thaloey, Jarinya RMUTT Data and Information Science Faculty of Science and Technology Pathum Thani Thailand RMUTT Big Data Analytics and Management Faculty of Science and Technology Pathum Thani Thailand

ISBN: (纸本)9798350391411

This research tackles the challenge of detecting hate speech and offensive content in political discussions on social media. By employing natural language processing (NLP) techniques, the study aims to contribute to creating healthier online communities. The researchers started with a manually labeled dataset of Reddit comments centered on political discourse. To address the common issue of class imbalance in classification tasks, they used an oversampling strategy to ensure equal representation of each class. Additionally, they applied data augmentation, specifically synonym replacement, to increase the diversity of the training data, which helps improve the model's generalization. For text preprocessing, the Bidirectional Encoder Represen- tations from Transformers (BERT) tokenizer was used. This tokenizer converts text into subwords, which is especially useful for handling out-of-vocabulary terms. The model used in this study is a fine-tuned version of BERT base, trained with specific hyperparameters: a learning rate of 5e-5, a batch size of 24, and a weight decay of 0.01. Training was carried out over seven epochs using the Hugging Face Transformers library and NVIDIA Tesla P100 GPUs available on Kaggle. The model's performance was assessed using 5-fold cross- validation, resulting in impressive metrics: an average accuracy of 99.34 percent, precision of 99.36 percent, recall of 99.34 percent, F1-score of 99.34 percent, and an ROC-AUC score of 99.96 percent. These results highlight the model's effectiveness in identifying hate speech and offensive content across different data splits. Compared to earlier research, this approach shows significant improvement in performance metrics. It demonstrates the potential of combining BERT with data augmentation and oversampling techniques for detecting hate speech in political discourse on Reddit. This study lays the groundwork for future research into real-time detection systems and cross-lingual models. © 2024 IEEE.

关键词： Natural language processing systems

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Detection Method of River Floating Objects based on Edge Computing 2

Detection Method of River Floating Objects based on Edge Com...

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2nd international conference on Robotics, Automation and intelligent Control, ICRAIC 2022

作者： Li, He Jia, Huimei Zhang, Rui Yang, Shuaipeng Qi, Qinglei Liu, Tianyang Henan Costar Group Co. Ltd Nanyang China College of Information Engineering Henan University of Science and Technology Luoyang China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China Sydney Business School Shanghai University Shanghai China

For the detection of objects floating in the river, most of the traditional intelligent video monitoring methods are used to monitor through manual viewing. During the traditional video data transmission and processing, the massive amount of data will put pressure on the background such as network and computing power. The new effective method is to crop and optimize YOLOV5s, add a specific image pre-processing module, and deploy it by edge computing, embed a SOC (System on Chip) chip in the web camera for real-time processing of video data. Simulation experiments prove that the above method can improve the accuracy and speed of the target detection network. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

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Improving Automatic Recognition of Emotional States Using EEG data Augmentation Techniques 27

Improving Automatic Recognition of Emotional States Using EE...

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27th international conference on Knowledge Based and intelligent Information and engineering Sytems, KES 2023

作者： Szczakowska, Patrycja Wosiak, Agnieszka Zykwinska, Katarzyna Lodz University of Technology Institute of Information Technology al. Politechniki 8 Lodz93-590 Poland

Emotion recognition is crucial for improving communication between humans and computers. Electroencephalography (EEG) signals can be used for this purpose, but the limited amount of available EEG data poses challenges in creating accurate classification models, particularly when using deep machine learning methods. To address this issue, we investigated the impact of data augmentation on the quality of emotion prediction for inter-subject classification based on public emotion EEG datasets, the MAHNOB. The effectiveness of different data augmentation techniques, including sliding windows of varying lengths, overlapping windows, and Gaussian noise, were tested. We verified the augmentation techniques by automated emotion classification using a shallow convolutional neural network and the Valence/Arousal model. The results demonstrate that data augmentation can significantly improve the accuracy of emotion prediction for individual subjects, with the noise method being the most effective. Augmentation using Gaussian noise achieves up to a 30% improvement for a single subject in both Arousal and Valence emotion model dimensions, compared to the baseline. Our findings highlight the potential of data augmentation as a promising approach for improving the accuracy of emotion recognition using EEG signals. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0)

关键词： augmentation techniques data preprocessing EEG analysis emotion recognition Gaussian noise

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PORTAL: Automatic Curricula Generation for Multiagent Reinforcement learning 22

PORTAL: Automatic Curricula Generation for Multiagent Reinfo...

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22nd international conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Wu, Jizhou Hao, Jianye Yang, Tianpei Zheng, Yan Taylor, Matthew E. Hao, Xiaotian Wang, Weixun College of Intelligence and Computing Tianjin University Tianjin China University of Alberta and Alberta Machine Intelligence Institute Edmonton Canada

Despite many breakthroughs in recent years, it is still hard for MultiAgent Reinforcement learning (MARL) algorithms to directly solve complex tasks in MultiAgent Systems (MASs) from scratch. In this work, we study how to use Automatic Curriculum learning (ACL) to reduce the number of environmental interactions required to learn a good policy. In order to solve a difficult task, ACL methods automatically select a sequence of tasks (i.e., curricula). The idea is to obtain maximum learning progress towards the final task by continuously learning on tasks that match the current capabilities of the learners. The key question is how to measure the learning progress of the learner for better curriculum selection. We propose a novel ACL framework, PrOgRessive mulTiagent Automatic curricuLum (PORTAL), for MASs. PORTAL selects curricula according to two criteria: 1) How difficult is a task, relative to the learners' current abilities? 2) How similar is a task, relative to the final task? By learning a shared feature space between tasks, PORTAL is able to characterize different tasks based on the distribution of features and select those that are similar to the final task. Also, the shared feature space can effectively facilitate the policy transfer between curricula. Experimental results show that PORTAL can train agents to master extremely hard cooperative tasks, which can not be achieved with previous state-of-the-art MARL algorithms. © 2023 international Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Curricula

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A Comparison of Machine learning Approaches for Predicting In-Car Display Production Quality 22nd

A Comparison of Machine Learning Approaches for Predicting I...

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22nd international conference on intelligent data engineering and automated learning

作者： Matos, Luis Miguel Domingues, Andre Moreira, Guilherme Cortez, Paulo Pilastri, Andre Univ Minho ALGORITMI Ctr Dept Informat Syst Guimaraes Portugal Bosch Car Multimedia Braga Portugal CCG ZGDV Inst EPMQ Guimaraes Portugal

ISBN: (纸本)9783030916077;9783030916084

In this paper, we explore eight Machine learning (ML) approaches (binary and one-class) to predict the quality of in-car displays, measured using Black Uniformity (BU) tests. During production, the industrial manufacturer routinely executes intermediate assembly (screwing and gluing) and functional tests that can signal potential causes for abnormal display units. By using these intermediate tests as inputs, the ML model can be used to identify the unknown relationships between intermediate and BU tests, helping to detect failure causes. In particular, we compare two sets of input variables (A and B) with hundreds of intermediate quality measures related with assembly and functional tests. Using recently collected industrial data, regarding around 147 thousand in-car display records, we performed two evaluation procedures, using first a time ordered train-test split and then a more robust rolling windows. Overall, the best predictive results (92%) were obtained using the full set of inputs (B) and an automated ML (AutoML) Stacked Ensemble (ASE). We further demonstrate the value of the selected ASE model, by selecting distinct decision threshold scenarios and by using a Sensitivity Analysis (SA) eXplainable Artificial Intelligence (XAI) method.

关键词： Anomaly detection automated machine learning Deep learning Explainable artificial intelligence Supervised learning One-class learning

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EduBot: A Proof-of-Concept for a High School Motivational Agent 23rd

EduBot: A Proof-of-Concept for a High School Motivational Ag...

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23rd international conference on intelligent data engineering and automated learning (IDEAL)

作者： Faria, Hugo Barbosa, Maria Araujo Veloso, Bruno Marcondes, Francisco S. Lima, Celso Duraes, Dalila Novais, Paulo Univ Minho ALGORITMI Ctr Braga Portugal High Sch Caldas Taipas Caldas Das Taipas Guimaraes Portugal

ISBN: (纸本)9783031217524;9783031217531

Motivation appears to plays a role in student dropout. This paper uses a dataset with high school information (756 entries) for creating a model that relates year failures with the other features on the dataset. The resulting model, based on XGBoost, shows that the top-ranked features are related with motivation issues corroborating with the initial observation. In this sense, a digital assistant embedded with a motivation module may aid on improving motivation and on avoiding dropout. In other words, if the predictor detects year failure possibility it start to act on motivating the student. Considering this paper being direct to underage students, this paper stops on the proof-of-concept level considering the actual dialogues and live tests are expected to performed with the support of educational psychologists.

关键词： Educational data mining Motivational assistant Predict at-risk students Student's performance

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Analysis of the Consumption and Sensors Features Contribution to the Consumption Forecast Using Explainable AI in Buildings

Analysis of the Consumption and Sensors Features Contributio...

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international conference on intelligent Systems Applications to Power Systems (ISAP)

作者： Daniel Ramos Pedro Faria Zita Vale Department of Informatics Engineering Polytechnic of Porto Porto Portugal

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

The energy sector nowadays has a high impact in the distribution of energy around the world. Moreover, the uncertainty of the energy and user behavior are a threat that causes the difficulty on the planning of an optimization plan intended to improve the energy efficiency as high as possible. data scientists and machine learning professionals recommend the use of deep learning and machine learning algorithms to train an historic with energy consumption from past time and additional variables (sensors of a building for example) to forecast the energy consumption for the intended target period. Several forecasting methods may be used such as Artificial Neural Networks, K-Nearest Neighbors, and XGBoost. Experts in the explainable artificial intelligence area recommend the use of methods such as Shapley Addictive Explanations (SHAP) in order to explain the input features of the historic dataset with a stronger influence on the output power consumption.

关键词： Training Energy consumption Correlation Uncertainty Buildings Sensor phenomena and characterization Power systems Forecasting intelligent sensors Power generation

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