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Towards Latent Space Optimization of GANs Using Meta-learning 21st

Towards Latent Space Optimization of GANs Using Meta-Learnin...

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Fontanini, Tomaso Pratico, Claudio Prati, Andrea Univ Parma Dept Engn & Architecture IMP Lab Parma Italy

ISBN: (纸本)9783031064272;9783031064265

The necessity to use very large datasets in order to train Generative Adversarial Networks (GANs) has limited their use in cases where the data at disposal are scarce or poorly labelled (e.g., in real life applications). Recently, meta-learning proved that it can help solving effectively few-shot classification problems, but its use in noise-to-image generation was only partially explored. In this paper, we took the first step into applying a meta-learning algorithm (Reptile), to the discriminator of a GAN and to a mapping network in order to optimize the random noise z to guide the generator network into producing images belonging to specific classes. By doing so, we prove that the latent space distribution is crucial for the generation of sharp samples when few training data are at disposal and also managed to generate samples of previously unseen classes just by optimizing the latent space without changing any parameter in the generator network. Finally, we show several experiments with two widely used datasets: MNIst and Omniglot.

关键词： Generative adversarial network Meta learning Few shot

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LOGIC: Probabilistic Machine learning for Time Series Classification 21

LOGIC: Probabilistic Machine Learning for Time Series Classi...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Berns, Fabian Huewel, Jan David Beecks, Christian Univ Munster Dept Comp Sci Munster Germany

ISBN: (纸本)9781665423984

Time series data is one of the complex data types commonly encountered in many application areas ranging from automotive, finance, medicine to industry. A prominent task is time series classification, which entails identifying expressive features in oder to predict class labels of time series data. In this paper, we propose a novel approach for time series classification called Local Gaussian Process Model Inference Classification (LOGIC). Our concept consists in (i) learning latent characteristics of given time series data by means of Gaussian processes, (ii) using these characteristics to embed time series into a more expressive feature space and (iii) classifying time series data based on these features via existing classification methods. By making use of various general-purpose classification methods, we show that LOGIC is able to compete with state-of-the-art approaches in terms of accuracy and efficiency.

关键词： Probabilistic Machine learning Gaussian Processes Time Series Classification

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Evaluating and Explaining Generative Adversarial Networks for Continual learning under Concept Drift 21

Evaluating and Explaining Generative Adversarial Networks fo...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Guzy, Filip Wozniak, Michal Krawczyk, Bartosz Wroclaw Univ Sci & Technol Dept Syst & Comp Networks Wroclaw Poland Virginia Commonwealth Univ Dept Comp Sci Sch Engn Richmond VA USA

ISBN: (纸本)9781665424271

Generative Adversarial Networks (GANs) are among the most popular contemporary machine learning algorithms. Despite remarkable successes in their developments, existing GANs cannot offer the appropriate tools to monitor their performance in a continual learning scenario when data distribution changes. We propose a complete framework for monitoring and evaluating GANs during the continual learning, explaining their reaction to the data distribution shifts. The proposed approach is the first complete solution for evaluating GANs in drifting environments, additionally adding explainability to the adaptation process. We introduce a novel prequential metric for continual evaluation of GANs. We show how to use various information extracted from streaming GANs to understand the model's behavior under data changes and gain insight into the nature of concept drift. Our explainable components focus on learning curves under non-stationary data that highlight retaining relevant and forgetting outdated information, as well as of dynamic visualization of changes in relevant regions for drifting images. The proposed tool allows for detecting changes in data, as well as evaluating and explaining the reaction of GANs to the concept shift. Our framework can be downloaded from a public repository https://***/w4k2/gan-data-streams.

关键词： deep learning continual learning data streams concept drift XAI generative models

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Smart Parking System with automated Vehicle Log Using Haar Cascade Classifier ANPR 6th

Smart Parking System with Automated Vehicle Log Using Haar ...

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Proceedings of the 6th IFIP TC 12 international conference on Computational Intelligence in data Science, ICCIDS 2023

作者： Gopikrishnan, S. Madduru, Abhiram Kalyan Karamsetty, Kaushik Ravuri, Dinesh Rohit School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9783031382956

Recent developments in intelligent transportation systems and GPUs for deep learning computation have made Automatic vehicle Number Plate Detection and Recognition (ANPR) a fascinating area for academic inquiry. ANPR has several transportation uses. Computer vision and AI systems might recognise car licences. This article discusses the automated Log System we constructed and the ANPR algorithm we utilised to get better results. This study detects licence plates using the Haar cascade classifier. OCR licence plate text. Haar feature-based cascaded classifiers are used to recognise and track automobiles in real-time. Haar characteristics are utilised to represent an image’s look. Calculated by removing the total of black and white pixels. The difference utilised to detect picture object existence. EasyOCR is our text extraction method. We constructed a MySQL database log system to record vehicle information and python computer vision technology to analyse and recognise licence plates in real time. © 2023, IFIP international Federation for Information Processing.

关键词： Optical character recognition

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Internet of Things Enabled Framework for Sustainable Mobility and Clean Environment in Smart Cities 1st

Internet of Things Enabled Framework for Sustainable Mobilit...

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1st international conference on Artificial Intelligence and Internet of Things, ICAII 2022

作者： Kaur, Surleen Sharma, Sandeep Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

ISBN: (纸本)9783031373022

With people moving to cities for permanent settlements, there has been a rapid rise in urbanization worldwide. To accommodate such a vast number of citizens and provide them with quality life, the existing infrastructure of the cities requires transitioning into a more intelligent and safer environment. Hence, the aim of smart cities is to utilize the resources in order to offer a healthy and sustainable habitat to its citizens. Due to the increased urbanization;two significant issues which exist and are likely to persist as the cities grow are: the constant increase in transportation and the deterioration of environment as it’s result. The ever-growing number of on-road vehicles eventually leads to increased emissions, which are detrimental to the environment and undermine the quality of life in the cities. This paper aims to explore Internet of Things-driven solutions to achieve sustainable mobility and to provide cleaner environment in smart cities. The implementation of intelligent services in smart cities is inextricably intertwined with the Internet of Things. IoT technologies such as sensors, communication gateways, cloud/edge computing, along with machine learning and artificial intelligence technologies, allow large-scale data collection, connectivity, storage, and processing;which facilitate availability of smart services through intelligent decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Internet of things

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Synthetic Slowness Shear Well-Log Prediction Using Supervised Machine learning Models 21st

Synthetic Slowness Shear Well-Log Prediction Using Supervise...

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21st international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Tamoto, Hugo Contreras, Rodrigo Colnago dos Santos, Franciso Lledo Viana, Monique Simplicio Gioria, Rafael dos Santos Carneiro, Cleyton de Carvalho Univ Sao Paulo Polytech Sch Dept Min & Petr Engn BR-11013560 Sao Paulo SP Brazil Sao Paulo State Univ Inst Biosci Letters & Exact Sci BR-15054000 Sao Paulo SP Brazil Mato Grosso State Univ Fac Engn & Architecture BR-78217900 Caceres MT Brazil Univ Fed Sao Carlos Dept Comp BR-13565905 Sao Carlos SP Brazil

ISBN: (纸本)9783031234910;9783031234927

The shear slowness well-log is a fundamental feature used in reservoir modeling, geomechanics, elastic properties, and borehole stability. This data is indirectly measured by well-logs and assists the geological, petrophysical, and geophysical subsurface characterization. However, the acquisition of shear slowness is not a standard procedure in thewell-logging program, especially in mature fields that have a limited logging scope. In this research, we propose to developmachine learning models to create synthetic shear slowness well-logs to fill this gap. We used standard well-log features such as natural gamma-ray, density log, neutron porosity, resistivity logs, and compressional slowness as input data to train the models, and successfully predicted a synthetic shear slowness well-log. Additionally, we created five supervised models using Neural Networks, AdaBoost, XGBoost, and CatBoost algorithms. Among all models created, the neural network algorithm provided the most optimized model, using multilayer perceptron architecture reaching impressive scores as R-2 of 0.9306, adjusted R-2 of 0.9304, and MSE less than 0.0694.

关键词： Synthetic well-logs Machine learning Regression models Forecasting Time-series

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End-To-End data Characteristics Monitoring for MLOps

End-To-End Data Characteristics Monitoring for MLOps

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international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Natnaree Jubju Natawut Nupairoj Department of Computer Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Machine learning models employ data for gathering insights, making decisions, and generating predictions. As inferenced data fed into the model may drift or shift over time, it may lead to model's performance degradation. Consequently, a model would require re-training. However, model evaluation and frequent re-training might be costly as ground truth labeling can be expensive. Therefore, monitoring data characteristics before and after model deployment can help choosing the appropriate time to re-train the model. This paper proposes a framework of end-to-end data characteristics monitoring within MLOps to provide a solution for smart retraining using a variety of tools for ease of use and cost- effectiveness.

关键词： Measurement Degradation Machine learning algorithms Software algorithms Machine learning Predictive models data models

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Predictions of Energy Consumption of Buildings' Life Cycle to Mitigate the Effects of Climate Change with a Focus on Energy Efficiency

Predictions of Energy Consumption of Buildings' Life Cycle t...

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2022 IEEE Pune Section international conference, PuneCon 2022

作者： Patil, Prajyot Pramod Sondkar, Shilpa Instrumentation Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9781665498975

Climate change and Energy are the prominent topics addressed by researchers in the 21st century. One of the concerns shown by the researchers is the amount of CO2 emitted by buildings across the globe. In 2020, the construction and demolition of buildings accounted for 37% of all energy-and process-related CO2emissions worldwide. There are methods to reduce the effect of buildings' construction and demolition on climate. intelligent infrastructure could be one of the solutions for energy efficiency. Energy Usage Intensity, which measures how much energy a building uses annually per total gross floor area, must be known before implementing any solutions. Contribution of this proposed work is to predict Energy Usage Intensity for buildings of different states in different weather conditions in the United states while comparing three different statistical models of machine learning to find best results of prediction. © 2022 IEEE.

关键词： Energy utilization

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Accurate Graph-Based PU learning without Class Prior 21

Accurate Graph-Based PU Learning without Class Prior

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Yoo, Jaemin Kim, Junghun Yoon, Hoyoung Kim, Geonsoo Jang, Changwon Kang, U. Seoul Natl Univ Seoul South Korea NCSOFT Seoul South Korea

ISBN: (纸本)9781665423984

How can we classify graph-structured data only with positive labels? Graph-based positive-unlabeled (PU) learning is to train a binary classifier given only the positive labels when the relationship between examples is given as a graph. The problem is of great importance for various tasks such as detecting malicious accounts in a social network, which are difficult to be modeled by supervised learning when the true negative labels are absent. Previous works for graph-based PU learning assume that the prior distribution of positive nodes is known in advance, which is not true in many real-world cases. In this work, we propose GRAB (Graph-based Risk minimization with iterAtive Belief propagation), a novel end-to-end approach for graph-based PU learning that requires no class prior. GRAB models a given graph as a Markov network and runs the marginalization and update steps iteratively. The marginalization step estimates the marginals of latent variables, while the update step trains a classifier network utilizing the computed priors in the objective function. Extensive experiments on five datasets show that GRAB achieves state-of-the-art accuracy, even compared with previous methods that are given the true prior.

关键词： PU learning graph neural networks loopy belief propagation Markov networks

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Application of Machine learning for Growth Environment Prediction in Agriculture 21

Application of Machine Learning for Growth Environment Predi...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Fujita, Momoko Yamada, Akari Susuki, Mao Makino, Hiroya Kita, Eisuke Nagoya Univ Grad Sch Informat Nagoya Aichi 4648601 Japan

ISBN: (纸本)9781665424271

It is very important to control the value of saturation in the adequate range in tomato cultivation in greenhouse. The prediction of the saturation value in greenhouse are discussed in this study. The mathematical model for prediction of saturation is defined by regression analysis of Ridge regression, Lasso regression, Random Forest and XGBoost. The results show that Random Forest and XGBoost has better accuracy than the others and that Random Forest is the best among them. The variable importance analysis shows that the indoor temperature, temperature inside greenhouse, is the most effective for the saturation. The prediction model is applied for controlling the saturation in the adequate range by changing the temperature.

关键词： Agriculture Saturation Prediction Control

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