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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Gorelik, Victor Zolotova, Tatiana Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Simulation Systems and Operations Research Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350337907

An assessment of the investor's risk profile is proposed as a risk coefficient in a model with a linear convolution of expected return and variance. The value of the risk coefficient is found from solving the optimization problem of the model with a limitation on profitability. The proposed method is implemented in the form of a software package using the example of the stock market. © 2023 IEEE.

关键词： Convolution

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DeFaktS: A German Dataset for Fine-Grained Disinformation Detection through Social Media Framing 30

DeFaktS: A German Dataset for Fine-Grained Disinformation De...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ashraf, Shaina Bezzaoui, Isabel Andone, Ionut Markowetz, Alexander Fegert, Jonas Flek, Lucie Department of Mathematics and Computer Science University of Marburg Germany FZI Forschungszentrum Informatik Germany Murmuras GmbH Germany Lab Germany University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (纸本)9782493814104

In today's rapidly evolving digital age, disinformation poses a significant threat to public sentiment and socio-political dynamics. To address this, we introduce a new dataset "DeFaktS", designed to understand and counter disinformation within German media. Distinctively curated across various news topics, DeFaktS offers an unparalleled insight into the diverse facets of disinformation. Our dataset, containing 105,855 posts with 20,008 meticulously labeled tweets, serves as a rich platform for in-depth exploration of disinformation's diverse characteristics. A key attribute that sets DeFaktS apart is, its fine-grain annotations based on polarized categories. Our annotation framework, grounded in the textual characteristics of news content, eliminates the need for external knowledge sources. Unlike most existing corpora that typically assign a singular global veracity value to news, our methodology seeks to annotate every structural component and semantic element of a news piece, ensuring a comprehensive and detailed understanding. In our experiments, we employed a mix of classical machine learning and advanced transformer-based models. The results underscored the potential of DeFaktS, with transformer models, especially the German variant of BERT, exhibiting pronounced effectiveness in both binary and fine-grained classifications. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Classification (of information)

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Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal Brain in 3D Ultrasound 22

Exploring Test Time Adaptation for Subcortical Segmentation ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Omolegan, Joshua Yeung, Pak Hei Wyburd, Madeleine K. Hesse, Linde Haak, Monique Namburete, Ana I. L. Dinsdale, Nicola K. University of Oxford Oxford Machine Learning in NeuroImaging Lab United Kingdom School of Computer Science and Engineering Nanyang Technological University Singapore Leiden University Medical Center Department of Obstetrics and Fetal Medicine Netherlands

ISBN: (纸本)9798331520526

Monitoring the growth of subcortical regions of the fetal brain in ultrasound (US) images can help identify the presence of abnormal development. Manually segmenting these regions is a challenging task, but recent work has shown that it can be automated using deep learning. However, applying pretrained models to unseen freehand US volumes often leads to a degradation of performance due to the vast differences in acquisition and alignment. In this work, we first demonstrate that test time adaptation (TTA) can be used to improve model performance in the presence of both real and simulated domain shifts. We further propose a novel TTA method by incorporating a normative atlas as a prior for anatomy. In the presence of various types of domain shifts, we benchmark the performance of different TTA methods and demonstrate the improvements brought by our proposed approach, which may further facilitate automated monitoring of fetal brain development. Our code is available at https://***/joshuaomolegan/TTA-for-3D-Fetal-Subcortical-Segmentation. © 2025 IEEE.

关键词： Segmentation Test Time Adaptation Ultrasound

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A Patient-Centric Blockchain Framework for Transparent and Secure Medical Data Sharing using Modified AES 2

A Patient-Centric Blockchain Framework for Transparent and S...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Meesala, Mohan Kumar Pareek, Piyush Kumar Addula, Santosh Reddy Shwetha, K.S. Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331504960

Due to its decentralised, accessible, and secured structure, blockchain-a framework that has historically been viewed with suspicion-has developed into a groundbreaking breakthrough. Reliable automated scripting and static data records are also a part of this system. The intricacy and expertise required to operate with blockchain technology is one of its disadvantages. machine learning (ML) frameworks are the key fixing this problem. It has the potential to make Blockchain more user-friendly. In this research paper, we reviewed the current state of blockchain-based cloud computing, machine learning, and safe data exchange. In this study, we suggest using a blockchain technology to provide a safe space where health entities can share medical information transparently. A modified advanced encryption standard (MAES) based blockchain encryption model is developed by us. This method is based on engineering and AI principles, and its main objective is to design useful artefacts that improve their respective domains. © 2024 IEEE.

关键词： Electronic health record

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Optimal parameter estimation of three solar cell models using modified spotted hyena optimization

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 361-372页

作者： Gafar, Mona El-Sehiemy, Ragab A. Hasanien, Hany M. Abaza, Amlak Department of Computer Science College of Science and Humanities in Al-Sulail Prince Sattam Bin Abdulaziz University Kharj Saudi Arabia Machine Learning and Information Retrieval Department Artificial Intelligence Kafrelsheikh University Kafr El Sheikh Egypt Electrical Engineering Department Faculty of Engineering Kafrelsheikh University Kafr El Sheikh Egypt Electrical Power and Machines Department Faculty of Engineering Ain Shams University Cairo11517 Egypt

This paper is concerned with identifying the optimal parameters of solar cell by using a modified spotted hyena optimization algorithm (MSHOA). In the MSHOA, the optimization process initializes random search agents then selects the best agents. The MSHOA modifies the original spotted hyena optimization algorithm by using an accelerating function, which improves the performance of reaching the optimal solution. The convergence occurs rapidly and realizes the global optimization with small iterations number. The numerical results obtained with different models emphasize that the MSHOA has the ability and stability to estimate the global optimal decision variables of solar cell module with a minimal root mean square error compared with other algorithms existed in literature. Moreover, three electrical models called single diode model, double diode model and triple diode model TDM are accurate for depicting electrical behavior of PV modules. It is truth to say that TDM is the most accurate model among the three models. The significant agreements between the estimated model using MSHOA, at different operating temperatures and irradiances, and the measured data assert on the effectiveness of the proposed algorithm and the accuracy of PV model. All results emphasize that the MSHOA is an effective and reliable optimization algorithm for finding the optimal parameters of solar cell modules. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Solar panels

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Sourcerer: sample-based maximum entropy source distribution estimation 24

Sourcerer: sample-based maximum entropy source distribution ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Julius Vetter Guy Moss Cornelius Schröder Richard Gao Jakob H. Macke Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen and Tübingen AI Center Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen and Tübingen AI Center and Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

ISBN: (纸本)9798331314385

Scientific modeling applications often require estimating a distribution of parameters consistent with a dataset of observations—an inference task also known as source distribution estimation. This problem can be ill-posed, however, since many different source distributions might produce the same distribution of data-consistent simulations. To make a principled choice among many equally valid sources, we propose an approach which targets the maximum entropy distribution, i.e., prioritizes retaining as much uncertainty as possible. Our method is purely sample-based—leveraging the Sliced-Wasserstein distance to measure the discrepancy between the dataset and simulations—and thus suitable for simulators with intractable likelihoods. We benchmark our method on several tasks, and show that it can recover source distributions with substantially higher entropy than recent source estimation methods, without sacrificing the fidelity of the simulations. Finally, to demonstrate the utility of our approach, we infer source distributions for parameters of the Hodgkin-Huxley model from experimental datasets with hundreds of single-neuron measurements. In summary, we propose a principled method for inferring source distributions of scientific simulator parameters while retaining as much uncertainty as possible.

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Robust Autonomous Vehicle Pursuit Without Expert Steering Labels

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IEEE Robotics and Automation Letters 2023年第10期8卷 6595-6602页

作者： Pan, Jiaxin Zhou, Changyao Gladkova, Mariia Khan, Qadeer Cremers, Daniel Technical University of Munich Computer Vision Group Garching85748 Germany Munich Data Science Institute Garching85748 Germany Munich Center for Machine Learning Munchen80333 Germany University of Oxford OxfordOX1 3AZ United Kingdom

In this work, we present a learning method for both lateral and longitudinal motion control of an ego-vehicle for the task of vehicle pursuit. The car being controlled does not have a pre-defined route, rather it reactively adapts to follow a target vehicle while maintaining a safety distance. To train our model, we do not rely on steering labels recorded from an expert driver, but effectively leverage a classical controller as an offline label generation tool. In addition, we account for the errors in the predicted control values, which can lead to a loss of tracking and catastrophic crashes of the controlled vehicle. To this end, we propose an effective data augmentation approach, which allows to train a network that is capable of handling different views of the target vehicle. During the pursuit, the target vehicle is firstly localized using a Convolutional Neural Network. The network takes a single RGB image along with cars' velocities and estimates target vehicle's pose with respect to the ego-vehicle. This information is then fed to a Multi-Layer Perceptron, which regresses the control commands for the ego-vehicle, namely throttle and steering angle. We extensively validate our approach using the CARLA simulator on a wide range of terrains. Our method demonstrates real-time performance, robustness to different scenarios including unseen trajectories and high route completion. © 2016 IEEE.

关键词： Deep learning

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Developing creative material in STEM courses using integrated engineering design based on APOS theory

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International Journal of Technology and Design Education 2023年第4期33卷 1627-1651页

作者： Zhong, Hua-Xu Lai, Chin-Feng Chang, Jui-Hung Chiu, Po-Sheng Department of Engineering Science National Cheng Kung University 1 University Rd. Tainan701 Taiwan Computer and Network Center Department of Computer Science and Information Engineering Department of Computer Science National Cheng Kung University 1 University Rd. Tainan701 Taiwan Department of E-Learning Design and Management National Chiayi University No. 85 Wenlong Minxiong Township Chiayi County 621 Taiwan

Over the past several years, creativity has been recognized as an important skill for success in STEM education, engineering design and computational thinking. There is limited research on how to apply Conceive, Design, Implement, Operate (CDIO) engineering design in STEM courses and how it affects students' creativity. This study strategically integrated computational thinking (CT), STEM, and CDIO engineering design, based on Action-Process-Object-Schema (APOS) theory, into a course. This study has adopted a framework to design creative learning materials and examined how this framework impacts students' creativity. This study used a pretest–posttest nonequivalent-groups design. The study investigated a STEM course which had 40 participants (n = 40, male = 11, female = 29) for 12 weeks. 6 weeks were spent using traditional teaching and 6 weeks integrating a CDIO framework into computational thinking. All the students in the course were college students with teacher college backgrounds. To examine the impact of our course, we used a questionnaire to examine the students’ creativity before and after the STEM course was applied. Our results show that our course had a significantly impact on our students’ creativity, particularly in the case of the male students. This finding is consistent with the findings of other research studies. This study also offers some suggestions for teachers who wish to improve their learning materials. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Students

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Smart Agriculture in the Era of Big Data: IoTAssisted Pest Forecasting and Resource Optimization for Sustainable Farming 2

Smart Agriculture in the Era of Big Data: IoTAssisted Pest F...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Kumar, Deepak Pawar, Priyanka Pramod Meesala, Mohan Kumar Pareek, Piyush Kumar Addula, Santosh Reddy Shwetha, K.S. Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331504960

The goal was to facilitate quick development and revitalisation of rural areas by utilising IoT knowledge to accomplish smart agriculture against the backdrop of big data. Depleted soil fertility, increased pest attacks, and diseases are some of the climate-related risks that the agricultural sector is battling. Predicting Utilising directly sensed environmental conditions as input, a machine learning model incorporating Random Forest and the Catboost algorithm determines, based on the current environmental conditions, whether pest populations are present or not. Next, Hybrid Weighted Ant Colony Optimisation (HWACO) is used to fine-tune the Catboost algorithm. This method evaluates the machine learning model's accuracy and precision in making predictions using test data. Predictions have been made using data from 2028 to 2022, a span of five years. Weekly forecasts are produced by the pest prediction model. With high F-measure, Precision, and Recall, the weekly predictions have an overall accuracy of 93%. Over time, the precision of the pest forecast steadily rises. Predictions for the next week are made by averaging all weather data from the past seven days. For immediate defences against pests, the weekly forecasts are crucial. The findings support sustainable farming methods and food security by showing that the device can maximise crop yields while decreasing resource consumption. © 2024 IEEE.

关键词： Smart agriculture

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IDENTIFYING COPELAND WINNERS IN DUELING BANDITS WITH INDIFFERENCES

arXiv

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arXiv 2023年

作者： Bengs, Viktor Haddenhorst, Björn Hüllermeier, Eyke Department of Computer Science Paderborn University Germany Germany Munich Center for Machine Learning Germany

We consider the task of identifying the Copeland winner(s) in a dueling bandits problem with ternary feedback. This is an underexplored but practically relevant variant of the conventional dueling bandits problem, in which, in addition to strict preference between two arms, one may observe feedback in the form of an indifference. We provide a lower bound on the sample complexity for any learning algorithm finding the Copeland winner(s) with a fixed error probability. Moreover, we propose POCOWISTA, an algorithm with a sample complexity that almost matches this lower bound, and which shows excellent empirical performance, even for the conventional dueling bandits problem. For the case where the preference probabilities satisfy a specific type of stochastic transitivity, we provide a refined version with an improved worst case sample *** Codes 68W27 (Primary) 68T05 (Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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