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Towards Neuroadaptive Augmented Reality Piano Tutorials 2

Towards Neuroadaptive Augmented Reality Piano Tutorials

2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Maitz, Florian Ribeiro Skreinig, Lucchas Kalkofen, Denis Wriessnegger, Selina C. Institute of Neural Engineering Graz University of Technology Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)9798350300802

EEG-based neuro-adaptive systems are becoming increasingly popular in the field of learning and education. Their ability to detect changes in brain activity in real-time makes them unique and enables monitoring mental workload (MWL) and fatigue. We present a neuro-adaptive system that combines an Augmented Reality (AR) piano tutorial with online EEG measurements of its user, delivered by a passive Brain computer Interface (BCI) system. The MWL was measured by means of EEG and a Filter Bank Common Spatial Patter algorithm (FBCSP) was trained to differentiate between low and high levels of MWL. Low levels were connected to the 0-back task and high levels to the 2-back task. The n-back task was a calibration task to train a binary Machine Learning (ML) classifier to differentiate between low and high MWL. This trained ML algorithm was then used as the central element of the passive BCI, which constantly classified small windows of the EEG during the piano tutorial and adapted the difficulty of the tutorial. 22 Participants were randomly separated into two groups: adaptive and non-adaptive piano tutorial. The results of the non-adaptive group showed significantly higher levels of classified MWL throughout the piano tutorial. © 2023 IEEE.

关键词： EEG mental workload Mixed Reality passive BCI

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Data loss in quantum image representation methods 27

Data loss in quantum image representation methods

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

作者： Werner, Krzysztof Kordasz, Michal Michalczuk, Agnieszka Cyran, Krzysztof Dept. of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice44-100 Poland

Quantum Image Representation is a well-studied area in quantum informatics. The first proposed algorithm was the qubit lattice method introduced in 2003. This method was not harnessing the full potential of the quantum system - using as many qubits as there were pixels in the image. The most commonly used methods right now are Flexible Representation of Quantum Images (FRQI) proposed in 2011, and Novel Enhanced Quantum Representation (NEQR) proposed in 2013, which uses superposition to encode a monochrome image on a quantum computer using a reduced number of qubits. In this paper, we want to compare these two methods, alongside Local Phase Image Quantum Encoding (LPIQE) method proposed in 2020, to see which method produces images with minimal data loss. Another comparison will be done with image encoded using the LPIQE method with Phase Distortion Unraveling (PDU) error mitigation method, which is done entirely on a classical computer. LPIQE implementation was intended for using the photonic quantum system since the phase-shift is easy to produce, and it will be implemented on real, photonic quantum computers after further development. © 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： FRQI LPIQE NEQR PDU quantum computing Quantum image encoding quantum information theory quantum sampling

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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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Transparency Distortion Robustness for SOTA Image Segmentation Tasks 4th

Transparency Distortion Robustness for SOTA Image Segmentat...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Knauthe, Volker Rak, Arne Wirth, Tristan Pöllabauer, Thomas Metzler, Simon Kuijper, Arjan Fellner, Dieter W. Technical University of Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany CGV Institute Graz University of Technology Graz Austria

ISBN: (纸本)9789819787043

Semantic Image Segmentation facilitates a multitude of real-world applications ranging from autonomous driving over industrial process supervision to vision aids for human beings. These models are usually trained in a supervised fashion using example inputs. Distribution Shifts between these examples and the inputs in operation may cause erroneous segmentations. The robustness of semantic segmentation models against distribution shifts caused by differing camera or lighting setups, lens distortions, adversarial inputs and image corruptions has been topic of recent research. However, robustness against spatially varying radial distortion effects that can be caused by uneven glass structures (e.g. windows) or the chaotic refraction in heated air has not been addressed by the research community yet. We propose a method to synthetically augment existing datasets with spatially varying distortions. Our experiments show, that these distortion effects degrade the performance of state-of-the-art segmentation models. Pretraining and enlarged model capacities proof to be suitable strategies for mitigating performance degradation to some degree, while fine-tuning on distorted images only leads to marginal performance improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantic Segmentation

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A SigFox-Based Blockchain Network for Electronic Health Records 6

A SigFox-Based Blockchain Network for Electronic Health Reco...

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6th International Conference on Algorithms, Computing and Systems, ICACS 2022

作者： Marar, Rosana Wael Computer Graphics and Animation Princess Sumaya University for Technology Amman Jordan

ISBN: (纸本)9781450397407

In the healthcare sector, accessibility. integrity, and privacy of patients' data is a primary concern. Traditionally, patients' medical history and records were saved on servers that are susceptible to cyber attacks and connectivity issues. In this study, a system that promotes interoperability and data interchange between healthcare providers whilst ensuring data confidentiality and privacy of patients' medical information is presented. This paper introduces an Electronic Healthcare Record (EHR) system using Blockchain structure that is connected using SigFox long-range wireless technology eliminating the need for third-party systems. Healthcare personnel can obtain, share, and edit patients' medical history across long distances while ensuring data integrity. The proposed system can be accessed via personal computers and mobile devices, hence, improving the system's reliability and performance. © 2022 ACM.

关键词： Blockchain

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Influence of Water Droplet Contamination for Transparency Segmentation 4th

Influence of Water Droplet Contamination for Transparency ...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Knauthe, Volker Weitz, Paul Pöllabauer, Thomas Wirth, Tristan Rak, Arne Kuijper, Arjan Fellner, Dieter W. Technical University of Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany CGV Institute Graz University of Technology Graz Austria

ISBN: (纸本)9789819787043

computer vision techniques are on the rise for industrial applications, like process supervision and autonomous agents, e.g., in the healthcare domain and dangerous environments. While the general usability of these techniques is high, there are still challenging real-world use-cases. Especially transparent structures, which can appear in the form of glass doors, protective casings or everyday objects like glasses, pose a challenge for computer vision methods. This paper evaluates the combination of transparent objects in conjunction with (naturally occurring) contamination through environmental effects like hazing. We introduce a novel publicly available dataset containing 489 images incorporating three grades of water droplet contamination on transparent structures and examine the resulting influence on transparency handling. Our findings show, that contaminated transparent objects are easier to segment and that we are able to distinguish between different severity levels of contamination with a current state-of-the art machine-learning model. This in turn opens up the possibility to enhance computer vision systems regarding resilience against, e.g., datashifts through contaminated protection casings or implement an automated cleaning alert. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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"Only the Old and Sick Will Die" - Reproducing 'Eugenic Visuality' in COVID-19 Data Visualization

"Only the Old and Sick Will Die" - Reproducing 'Eugenic Visu...

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2022 IEEE International Symposium on technology and Society, ISTAS 2022

作者： Williams, Rua M. Purdue University Computer Graphics Technology West LafayetteIN United States

ISBN: (纸本)9781665484107

COVID-19 illness and death has disproportionately impacted marginalized groups the world over. In the United States, Black and Indigenous people have endured the largest risk of death. Disabled and chronically ill people have continued to isolate as their peers 'return to normal', bearing sole liability for their own safety in a society that deems their lives not worth the 'sacrifice' of public health measures. While public and institutional policy makers bare personal responsibility for 'survival of the fittest' approaches to public health, data science and visualization has contributed to and legitimized many of these eugenic policy decisions through design tropes I characterize as 'eugenic visuality'. In this paper, I explore how inadequacies and obscurities in COVID-19 data visualization have contributed to and sustained public narratives that devalue marginalized lives for the comfort of white-supremacist and capitalist social norms. While I focus on visualizations and statements provided by the CDC, the implications extend beyond any individual or institution to our collective preconceptions and values. Namely, unexamined biases and unquestioned norms are embedded in data science and visualization, constraining how data is represented and interpreted. These assumptions limit how data can be leveraged in the pursuit of just social policy. Therefore, I propose guiding principles for a Just Visuality in data science and representation, supported by the work of disabled activists and scholars of color. © 2022 IEEE.

关键词： COVID-19

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Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

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computers, Materials & Continua 2023年第8期76卷 1947-1976页

作者： Asif Rahim Yanru Zhong Tariq Ahmad Sadique Ahmad Mohammed A.ElAffendi School of Computer and Information Security Guilin University of Electronic TechnologyGuilin541004China Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic TechnologyGuilin541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of *** Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of *** this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship *** conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 *** compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN *** results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like *** findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this ***,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic *** findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

关键词： Convolutional Neural Network(CNN) hyper-tuning authorship verification digital forensics

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The Ability of Pretrained Large Language Models in Understanding Health Concepts in Social Media Posts

The Ability of Pretrained Large Language Models in Understan...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Keyuan Bernard, Gordon R. Purdue University Northwest Department of Computer Information Technology & Graphics HammondIN United States Vanderbilt University Department of Medicine NashvilleTN United States

ISBN: (纸本)9798350386226

Lay people's terms of health concepts are widely found in social medial data. Understanding their terms can help learn what they have experienced. The existing consumer health vocabularies do not include many health concept phrases found in social media. Recent advancement of pretrained large language models has demonstrated their state-of-the-art performance in a number of natural language processing tasks. In this work, we investigated 3 commonly used pretrained large language models on their ability of understanding consumer health concepts. The results on a corpus of 156 phrases related to 3 common COVID-19 symptoms showed that these pretrained general domain language models generated encouraging outputs, indicating that they may be a promising alternative to the current consumer health vocabularies. © 2024 IEEE.

关键词： Electronic health record

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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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