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arXiv 2023年

作者： Wimalawarne, Kishan Suzuki, Taiji Langer, Sophie Department of Mathematical Informatics The University of Tokyo Tokyo Japan RIKEN Tokyo Japan Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Learning the Green’s function using deep learning models enables to solve different classes of partial differential equations. A practical limitation of using deep learning for the Green’s function is the repeated computationally expensive Monte-Carlo integral approximations. We propose to learn the Green’s function by low-rank decomposition, which results in a novel architecture to remove redundant computations by separate learning with domain data for evaluation and Monte-Carlo samples for integral approximation. Using experiments we show that the proposed method improves computational time compared to MOD-Net while achieving comparable accuracy compared to both PINNs and MOD-Net. © 2023, CC BY.

关键词： Monte Carlo methods

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Runtime Verification for Trustworthy Computing

arXiv

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arXiv 2023年

作者： Abela, Robert Colombo, Christian Curmi, Axel Fenech, Mattea Vella, Mark Ferrando, Angelo Department of Computer Science Faculty of ICT University of Malta Msida Malta Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genova16145 Italy

Autonomous and robotic systems are increasingly being trusted with sensitive activities with potentially serious consequences if that trust is broken. Runtime verification techniques present a natural source of inspiration for monitoring and enforcing the desirable properties of the communication protocols in place, providing a formal basis and ways to limit intrusiveness. A recently proposed approach, RV-TEE, shows how runtime verification can enhance the level of trust to the Rich Execution Environment (REE), consequently adding a further layer of protection around the Trusted Execution Environment (TEE). By reflecting on the implication of deploying RV in the context of trustworthy computing, we propose practical solutions to two threat models for the RV-TEE monitoring process: one where the adversary has gained access to the system without elevated privileges, and another where the adversary gains all privileges to the host system but fails to steal secrets from the TEE. © 2023, CC BY.

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Reinforcement Learning for Orientation on the Lie Algebra

Reinforcement Learning for Orientation on the Lie Algebra

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IEEE Signal Processing and Communications Applications (SIU)

作者： Naseem Alhousani Hatice Kose Fares J. Abu-Dakka Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Munich Institute of Robotics and Machine Intelligence Technische Universität München Germany

In this paper, we propose a novel framework for Reinforcement Learning on Lie algebra and show how it applies to learning the orientation of the robot's end effector in the task space. The proposed framework is suitable for model-free Reinforcement learning algorithms. Our research is motivated by the fact that in robotics, non-Euclidean data (e.g., orientation) is common in learning manipulation skills, yet neglecting the geometric meaning of such data affects learning performance and accuracy. In particular, our innovation is to apply policy parameterization and learning on the Lie algebra, then map back the learned actions to the hemisphere manifold. The proposed framework opens the door for some model-free Reinforcement learning algorithms designed for Euclidean space to learn non-Euclidean data without change. According to the best of our knowledge, this research work is the first effort in applying a policy parameterization in the context of Reinforcement learning on the Lie algebra of the hemisphere manifold. The results of our experiments provide evidence to support our hypothesis that learning orientation on the Lie algebra is more precise and leads to a superior solution than learning through the normalization of non-Euclidean data.

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Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis

Machine Learning-Based Cascade Filtering System for fNIRS Da...

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2023 Progress in Applied Electrical engineering, PAEE 2023

作者： Pelc, Mariusz Mikolajewski, Dariusz Ruotsalo, Tuukka Leiva, Luis A. Sudol, Adam Gorzelanczyk, Edward Jacek Lysiak, Adam Kawala-Sterniuk, Aleksandra Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Helsinki Finland University of Copenhagen Denmark Faulty of Natural and Technical Sciences University of Opole Opole Poland University of Opole Faculty of Natural and Technical Sciences Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectroscopy (fNIRS), which shows the level of oxygenation in the brain and, unlike EEG signals (showing electrical brain activity), are less prone to potential interference, disturbances or artifacts occurrence. © 2023 IEEE.

关键词： Machine learning

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Implementation of Convolutional Neural Network for COVID19 Screening using X-Rays Images

Implementation of Convolutional Neural Network for COVID19 S...

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computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Mera Kartika Delimayanti Anggi Mardiyono Bambang Warsuta Eka Suci Puspitaningrum Rizqi Fitri Naryanto Agustien Naryaningsih Department of Computer and Informatics Engineering Politeknik Negeri Jakarta Indonesia Department of Mechanical Engineering Faculty of Engineering Universitas Negeri Semarang Indonesia Fisheries Research Center National Research and Innovation Agency (BRIN) Indonesia

Various unwelcome conditions have existed since the introduction of the novel coronavirus illness (COVID-19). COVID-19 can cause fever, muscle soreness, shortness of breath, cough, headache, and other symptoms. Diagnosis at an early stage is a crucial aspect of successful treatment. Therefore, it is necessary to seek out alternate methods for COVID-19 detection. Among the existing imaging resources, X-ray images are generally accessible and inexpensive. Consequently, an alternate diagnostic tool for detecting COVID-19 instances is provided using available resources. In the first stages of COVID-19, X-rays detected the disease before it spread to the lungs and caused more damage. Machine learning models can help clinicians accomplish jobs more quickly and accurately. In addition to chest X-ray pictures and fundus images, deep learning algorithms have been used to diagnose illnesses. This research was conducted to classify the X-ray chest images in COVID-19 and normal cases based on the public datasets which were used. This analysis uses 5600 images from the accessible resources, and a Convolutional Neural Network (CNN) architecture with the VGG16 algorithm was employed to diagnose COVID19. VGG16 is object identification and classification method that can classify with greater precision than most other deep learning algorithms. Transfer learning and fine-tuning were employed to help for improving the performance. The results showed that the VGG16 network had an accuracy of 98.13%. This research has implications for the early detection of COVID-19 by using X-ray images. The experiment and analysis reveal our suggested method's promising and stable performance compared to the current standard.

关键词： COVID-19 Deep learning Transfer learning computer architecture Classification algorithms Convolutional neural networks Task analysis

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Deep learning-based real time detection for cardiac objects with fetal ultrasound video

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informatics in Medicine Unlocked 2023年 36卷

作者： Iriani Sapitri, Ade Nurmaini, Siti Naufal Rachmatullah, Muhammad Tutuko, Bambang Darmawahyuni, Annisa Firdaus, Firdaus Rini, Dian Palupi Islami, Anggun Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang 30139 Indonesia Department of Informatics Engineering Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia

Fetal cardiac anatomical structure interpretation by ultrasound (US) is a key part of prenatal assessment. Unfortunately, the numerous speckles in US video, the small size of fetal cardiac structures, and unfixed fetal positions make manual detection processes difficult. To alleviate such problems, a deep learning model was developed to fully automate the processing of fetal echocardiographs with 2-dimensional cross-sectional images. The weakness of such an approach is that a physician immediately interprets US video with 3-dimensional objects to diagnose patients in clinical practice. To improve fetal cardiac anatomical structure interpretation via US for accurate and real time diagnosis, this paper proposes a real-time fetal cardiac substructure detection using US video with the You Only Look Once (YOLO) framework. In YOLO, an end-to-end neural network makes predictions for cardiac substructure objects, boxes, and class probabilities simultaneously. To achieve reliable performance, 40 fetal echocardiography videos were trained with the new YOLOv7 architecture and fine-tuned to work optimally and run efficiently. We conducted with nine fetal cardiac substructure objects such as the left atrium, right atrium, left ventricle, right ventricle, tricuspid valve, pulmonary valve, mitral valve, aortic valve, and aorta. The results yielded the highest mean average precision of 82.10%, reaching 17 frames per second (FPS) for nine cardiac substructure objects in 0.3 ms. The main finding of our study is that with even a limited number of US videos, YOLOv7 can detect fetal cardiac substructure objects in real-time. Such a network can work efficiently to detect small fetal cardiac objects automatically in a rapid phase with the help of proper fine-tuning. This work mainly assists medical experts in the fetal cardiac anatomy diagnostic process. © 2022 The Authors

关键词： computer vision Fetal echocardiography Object detection YOLOv7

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Enhanced bat algorithm for COVID-19 short-term forecasting using optimized LSTM

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Soft Computing 2021年第20期25卷 12989-12999页

作者： Rauf, Hafiz Tayyab Gao, Jiechao Almadhor, Ahmad Arif, Muhammad Nafis, Md Tabrez Department of Computer Science Faculty of Engineering & Informatics University of BRADFORD Bradford United Kingdom Department of Computer Science University of Virginia Charlottesville Virginia United States Department of Computer Engineering and Networks Jouf University Sakakah Saudi Arabia School of Computer Science Guangzhou University Guangzhou 510006 China Department of Computer Science and Engineering Jamia Hamdard New Delhi India

The highly infectious COVID-19 critically affected the world that has stuck millions of citizens in their homes to avoid possible spreading of the disease. Researchers in different fields are continually working to develop vaccines and prevention strategies. However, an accurate forecast of the outbreak can help control the pandemic until a vaccine is available. Several machine learning and deep learning-based approaches are available to forecast the confirmed cases, but they lack the optimized temporal component and nonlinearity. To enhance the current forecasting frameworks’ capability, we proposed optimized long short-term memory networks (LSTM) to forecast COVID-19 cases and reduce mean absolute error. For the optimization of LSTM, we applied bat algorithm. Furthermore, to tackle the premature convergence and local minima problem of BA, we proposed an enhanced variant of BA. The proposed version utilized Gaussian adaptive inertia weight to control the individual velocity in the entire swarm. In addition, we substitute random walk with the Gaussian walk to observe the local search mechanism. The proposed LSTM examines the personal best solution with the swarm’s local best and preserves the optimal solution by combining the Gaussian walk. To evaluate the optimized LSTM, we compared it with the non-optimal version of LSTM, recurrent neural network, gated recurrent units, and other recent state-of-the-art algorithms. The experimental results prove the superiority of the optimized LSTM over other recent algorithms by obtaining 99.52 % accuracy. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19 Gaussian distribution Gaussian inertia weight LSTM

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Unveiling the Black Box: Investigating the Interplay between AI Technologies, Explainability, and Legal Implications

Unveiling the Black Box: Investigating the Interplay between...

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International Conference on computer Science and engineering (UBMK)

作者： Cihan Ardoğanyilmaz Berkay Mengünoğul Muhammet Balci Computer Engineering Department Marmara University Istanbul Türkiye Informatics Northeastern University Massachusetts USA Law Faculty Bahcesehir University Istanbul Türkiye

Discovering patterns is the process of understanding, while explainability refers to the ability to represent the discovered patterns in a way that the target audience can comprehend. If we adapt the relevant proposition to explainability in law, jurists initially extract the essential components from a concrete case and then establish meaningful connections among these components to reach a conclusion. The second stage, where they explain how they reached the conclusion through the connections established between the components, is referred to as generating reasoning. This study focuses on the first stage of explainability, which involves extracting significant components and establishing basic-level connections with the conclusion. The proposed methodology presents an explainability approach that is independent of the model and can be applied to all classification problems. Furthermore, the best performance in the literature is achieved on the task of predicting the outcomes of Turkish Constitutional Court decisions.

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Adaptive Forwarding Strategy in Named Data Networking : A Survey 15

Adaptive Forwarding Strategy in Named Data Networking : A Su...

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15th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2021

作者： Ahdan, Syaiful Nurhayati, Ade Nurkahfi, Galih Nugraha Syambas, Nana Rachmana School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Lampung 35132 Indonesia Institut Teknologi Telkom Jakarta Faculty of Engineering Jakarta11710 Indonesia

ISBN: (纸本)9781665428927

Named Data Networking (NDN) is a new network Architecture, the design principle of NDN comes from the success of today's internet, NDN changes the current network paradigm, sending a packet to be sent to the destination address based on the name, Strategy Forwarding is a decision making for the purpose of forwarding, when and where the interest will be forwarded. In contrast to the current concept of IP addresses, NDN uses Strategy Forwarding (SF) as the concept used in sending packets to their destination. And this is the purpose of writing this paper. This paper discusses some of the results of Strategy Forwarding from previous research. Each application will require different forwarding behavior, to know the characteristics of each proposed forwarding, this paper will present information about the measurement metrics and information about the topological model used in each implementation of the proposed forwarding strategy. The measurement metrics section also presents a summary in a comparison chart, and a classification of the proposed forwarding strategy. 50% on testing the Strategy Forwarding using interest metrics, 32% Delay, 29% Data, 21% Hop count and Delivery, and 57% Other Metrics. © 2021 IEEE.

关键词： Network architecture

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