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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Balas, Zuzanna Tokarz, Krzysztof Zielinski, Bartlomiej Guzniczak, Tomasz Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

In the paper, the authors check the behaviour of Bluetooth Low Energy protocol in a popular smart wristband and a microcontroller in the heart rate monitoring application. The measurements were collected using a development board with the ESP32 System on Chip. The authors tested measurement period stability and measurement reliability in various conditions. © 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： Bluetooth Low Energy Health monitoring Internet of Things Smart wristband Wireless data transmission

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Observations of data characteristics and irregularities through domain-oriented transformations of attributes 28th

Observations of data characteristics and irregularities thro...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Stanczyk, Urszula Baron, Grzegorz Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper presents research dedicated to observations of relations between attribute properties and discretisation. In the investigations described, the gradually increasing sets of features were discretised by selected approaches, and several variants of data were constructed. The continuous, partially discrete, and completely translated datasets were explored by the chosen classifiers and their performance studied in the context of a number of discretised attributes, discretisation procedures, and the way of processing of features and datasets. The stylometric problem of authorship attribution was the machine learning task under study. The experimental results enable to observe closer the specificity of style-markers employed as characteristic features, and indicate conditions for efficient recognition of authorship. They can be extended to other application domains with similar characteristics. © 2024 The Authors.

关键词： Data assimilation

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Domain-specific data characteristics: A study on meaning of stylometric sub-concepts and in-class imbalance 28th

Domain-specific data characteristics: A study on meaning of ...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Stanczyk, Urszula Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

In the context of data imbalance probably the most investigated problem is imbalance of classes, as learning from the data with this characteristic makes detection of existing patterns for all classes more difficult. However, other problems related to imbalance also exists and the paper addresses such cases where classes are balanced, but there is in-class imbalance. Such imbalance can be caused by uneven representation of sub-concepts. When there is a noticeable difference between the numbers of samples belonging to sub-concepts, this can turn the under-represented sub-concepts into disjuncts. Data irregularities of this type can hinder recognition, therefore actions are typically taken to restore balance. In the investigations described, the issue was studied in the stylometric domain and various classifiers were applied to the data that was balanced, then imbalanced, and finally with restored balance. The experiments show that the specifics of the domain of application can put its own mark on the data which is difficult to overcome by standard processing such as under- or oversampling. Observed dependence on a learner and dataset makes the issue even more complex and layered, and shows the need for deeper studies. © 2024 The Authors.

关键词： Data assimilation

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WEB-ANEULYSIS: A Web-based Application for the Analysis of Aneurysm Data

WEB-ANEULYSIS: A Web-based Application for the Analysis of A...

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Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Preßler, Rebecca Meuschke, Monique Voigt, Henrik Lawonn, Kai Institute of Computer Science University of Jena Germany Department of Simulation and Graphics University of Magdeburg Germany

ISBN: (纸本)9783658416560

The analysis and treatment of cerebral aneurysms are important in neuroradiology. To support physicians in these tasks a desktop application for the visual analysis of aneurysm data called Aneulysis was developed. Because desktop applications complicate collaborative data exploration by physicians working at different locations,we present Web-Aneulysis, aweb-based version of Aneulysis written with the *** library. Especially the collaborative treatment planning is supported by our application. Therefore, Web-Aneulysis provides features for calculating morphological descriptors, positioning landmarks on the vessel surface, and deploying medical implants inside the vessel. We used Web-Aneulysis for the treatment planning of different cerebral aneurysm data sets and performed a qualitative evaluation together with two neuroradiologists. © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

关键词： Web services

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Deep Hierarchical Variational Autoencoders for World Models in Reinforcement Learning 5

Deep Hierarchical Variational Autoencoders for World Models ...

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5th International Conference on Transdisciplinary AI, TransAI 2023

作者： Ayyalasomayajula, Sriharshitha Rekabdar, Banafsheh Mousas, Christos Portland State University Department of Computer Science PortlandOR United States Purdue University Department of Computer Graphics Technology West LafayetteIN United States

ISBN: (纸本)9798350358018

With the increasing demand for sample-efficient and robust reinforcement learning agents, particularly in intricate domains like robotics, healthcare, and gaming, there is a strong need to minimize the computational overhead caused by the interactions between real and virtual agents. This necessitates highly accurate models to simulate virtual agents and limit the number of such interactions. To this effect, model-based reinforcement learning (MBRL) has been proven very effective in formulating an environment with superior decision-making and higher learning efficiency. A known approach in MBRL is World Models, which uses a generative engine called Variational Autoencoders (VAE). VAE utilizes a relatively simple architecture constrained in processing power for complex image inputs. Therefore, the image reconstruction error is high. Recent research in VAEs has shown poor reconstruction quality. This paper proposes a Nouveau VAE (NVAE) based World Models to address the abovementioned limitations. NVAE, which employs deep convolutions in its architecture, is employed as the visual sensory component of the World Models and is used to encode the environment dynamics into a latent representation. We show that NVAE-based World Models perform exceptionally well in the dream environment of car racing-v2 (OpenAI GYM env), improving the agent's performance by 45%. We then demonstrate that the NVAE-based World Models can be applied to robotic simulation environments like panda-gym, where the agent achieved a 95 % success rate in solving the reach task. © 2023 IEEE.

关键词： Image reconstruction

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Estimation of the Impact of Dimensionality Reduction of the Feature Space on the Efficiency of Movements Classification Based on Surface Electromyography 5

Estimation of the Impact of Dimensionality Reduction of the ...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Kuznetsov, Ivan V. Ponomarchuk, Yulia V. Far Eastern State Transport University Department of Computer Science and Computer Graphics Khabarovsk Russia Far Eastern State Transport University Faculty of Control Automation and Telecommunications Khabarovsk Russia

ISBN: (纸本)9798350363739

One of the actual tasks of movement classification based on electromyography is the choice of the most informative set of features characterizing the movements. Increasing the number of features may decrease the learning performance of classifiers and reduce the efficiency of gesture classification. This paper is devoted to study the possibilities of improving the movement's classification efficiency by using the feature space dimensionality reduction methods. Classifiers based on Support Vector Machines (SVM)., Decision Trees (DT)., Random Forest (RF) and K-nearest neighbors (KNN) were considered., and Principal Component Analysis method (PCA) was used to dimensionality reduction. 3% improvement in classification performance was achieved using PCA and KNN. © 2024 IEEE.

关键词： Support vector machines

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Minimum Partition into Plane Subgraphs: The CG:SHOP Challenge 2022

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ACM Journal of Experimental Algorithmics 2023年第3期28卷 1-13页

作者： Fekete, Sándor P. Keldenich, Phillip Krupke, Dominik Schirra, Stefan Department of Computer Science TU Braunschweig Mühlenpfordtstr. 23 Braunschweig38106 Germany Department for Simulation and Graphics OVGU Magdeburg Universitätsplatz 2 Magdeburg39106 Germany

We give an overview of the 2022 Computational Geometry Challenge targeting the problem Minimum Partition into Plane Subsets, which consists of partitioning a given set of line segments into a minimum number of non-cro... 详细信息

关键词： Computational geometry geometric optimization algorithm engineering graph coloring plane subgraphs intersection graphs CG:SHOP

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Optimizing Artificial Neural Networks Trough Weight Adjustments 28th

Optimizing Artificial Neural Networks Trough Weight Adjustme...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Akber, Syed Muhammad Abrar Szczesna, Agnieszka Kazmi, Sadia Nishat Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Longer training times pose a significant challenge in artificial neural networks (ANNs) as it may leads to increasing the computational costs and decreasing the effectiveness of the model. Therefore, it is imperative to reduce training times in ANNs to enhance the computational efficiency. The initialization of the weights between the layers in ANN plays a vital role in reducing training times. Appropriate weight initialization can help the network converge faster during the training by providing an optimum starting point for the network. Therefore, weight initialization techniques are essential for efficient training of ANNs. This paper revisits and implements different popular weight initialization techniques in ANNs and analyzes their impact on training time. Specifically, this paper implements Gaussian-based, Kaming-based, and Xavier-based weight initiation atop a popular DNN-based network. The experiments are conducted by employing a well-known dataset. The results show that the scenario when no weight initiation is applied consumed the highest training time, whereas different weight initiation techniques contribute in reducing the training times for the network. © 2024 The Authors.

关键词： Deep neural networks

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P2LNet: HD Map Validation Using Graph Neural Networks 1

P2LNet: HD Map Validation Using Graph Neural Networks

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1st International Conference on Robotics, Engineering, science, and Technology, RESTCON 2024

作者： Reji, Jeevan Omanwar, Vaibhav Birla Institute of Technology & Sciences Pilani Department of Computer Science Hyderabad Campus India Nvidia Graphics Pvt Ltd Pune India

ISBN: (纸本)9798350348842

HD Maps are a highly important part of the autonomous driving stack to perform the activities of localization and route planning on the road. Therefore, HD Map Validation is crucial to ensure the HD Maps represent the road information as accurately as possible. While heuristics have been developed to validate HD Maps with reasonable accuracy, they are unable to solve the challenging path to traffic light validation problem of HD Map junctions. Based on the success of Graph Neural Network approaches on HD Map problems, such as Vector net, we propose the P2LNet. This P2LNet architecture consists of a fully connected subgraph followed by a Graph Encoder-Decoder Architecture that finally predicts the correct associations that would exist between paths and traffic lights in the junction. We trained and evaluated P2LNet on our in-house HD Map junction dataset, with P2LNet showing 94% accuracy on predicting the correct labels for the associations from a test set. While the results could be further improved by using edge features, P2LNet provides a significant accuracy in validating incorrect light associations. It also shows how GNN based approaches can be used to solve other significant HD Map and validation issues. © 2024 IEEE.

关键词： Autonomous vehicles

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DNN-based Skeleton Independent Motion Retargeting 27

DNN-based Skeleton Independent Motion Retargeting

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Akber, Syed Muhammad Abrar Kazmi, Sadia Nishat Szczesna, Agnieszka Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

In the modern digital world, the use of animated applications has increased significantly and such applications have quietly become an integral part of life. Capturing human motions is a fundamental aspect of these applications which is performed by motion-capturing (MoCap) systems. Due to the heterogeneity of MoCap systems and marker configurations, the captured skeletons may vary in their structure, number of joints, and bone lengths. The need for motion retargeting arises to transfer motion captured from one articulated actor to another. Although motion retargeting is a long-standing issue, current methods cannot automatically execute retargeting between skeletons. This paper presents the retraining of a popular motion retargeting network with a new large-scale motion dataset. The retraining aims to demonstrate the capabilities of the motion retargeting network to handle the problem of motion retargeting between skeletons that may have different structures. The paper first, presents the motion retargeting network that employs an encoder-decoder architecture. Afterward, we re-train the motion retargeting network with a new MoCap dataset. To validate the effectiveness of the network, we visually compare the generated results with the existing retargeting model. The results show that the motion retargeting network effectively retargets the different motions. © 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： Decoder Deep neural network Encoder Motion retargeting

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