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Integration of Artificial Intelligence in the Organizational Adoption – A Configurational Perspective 42

Integration of Artificial Intelligence in the Organizational...

42nd International Conference on Information Systems: Building Sustainability and Resilience with IS: A Call for Action, ICIS 2021 TREOs

作者： Laut, Philipp Dumbach, Philipp Eskofier, Bjoern M. University of Bamberg Kärntenstraße 7 Bamberg96047 Germany Machine Learning and Data Analytics Lab Carl-Thiersch-Straße 2b Erlangen91052 Germany

ISBN: (纸本)9781713893608

The adoption of artificial intelligence (AI) within organizations is experiencing growing interest. Since AI has distinctive characteristics (automation – augmentation), it is unclear how both characteristics influence the adoption. Besides, current research calls for a configurational perspective within the adoption theory. Building on these research gaps, we use the technology-organization-environment framework (TOE) and combine the AI characteristics ‘automation – augmentation’ and ‘product or service – process’ with the organizational and environmental characteristics: differentiation strategy, resources, entrepreneurial orientation, and network support. We collected a sample of 104 questionnairesfrom top-levelandmiddle-levelmanagersaswellas IT experts. Using fuzzy set Qualitative Comparative Analysis (fsQCA), we reveal four different solutions, namely internal integrator, supportive provider, powerful innovator, and rising enthusiast, that explain the adoption of AI. Moreover, we show that organizations adopting AI have an explicit focus on AI characteristics. Furthermore, we underline the importance of configurational thinking within the adoption. © 2021 42nd International Conference on Information Systems, ICIS 2021 TREOs: "Building Sustainability and Resilience with IS: A Call for Action". All Rights Reserved.

关键词： Automation

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Radar-Based Tremor Quantification Using Deep learning for Improved Parkinson's and Palliative Care Assessment

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2024年 2卷 1174-1185页

作者： Mejdani, Desar Braunig, Johanna Griebhammer, Stefan G. Krauss, Daniel Steigleder, Tobias Engel, Lukas Jukic, Jelena Rozhdestvenskaya, Anna Winkler, Jurgen Eskofier, Bjoern Ostgathe, Christoph Vossiek, Martin Institute of Microwaves and Photonics Erlangen91058 Germany Machine Learning and Data Analytics Laboratory Erlangen91058 Germany University Hospital Erlangen Department of Palliative Medicine Erlangen91054 Germany University Hospital Erlangen Department of Molecular Neurology Erlangen91054 Germany

Tremor is one of the most prevalent movement disorders, which is especially observed in patients with Parkinson's disease (PD) and other conditions common in palliative care (PC). Effective treatment and monitoring of disease progression are crucial in the context of PC for patients suffering from movement disorders. To this aim, accurate and continuous detection and assessment of tremor characteristics, such as the tremor frequency, is required. Current evaluations by clinicians conducted during sporadic consultations are subjective and intermittent. Radar sensors provide continuous, objective evaluations of tremor motion in patient monitoring, offering a contactless, light-independent, and privacy-preserving method that directly measures tremor's radial motion through the Doppler effect. As previous radar-based research lacks continuous tremor monitoring in realistic scenarios, this study uses a frequency-modulated continuous-wave (FMCW) radar to detect subtle tremor motions and estimates their frequencies amid challenges such as large body motion interference in a clinical setting. Seventeen healthy participants were instructed to mimic tremors in their right hand while performing three diagnostics movements frequently used in tremor assessment, and two activities that were inspired by common daily tasks encountered in PC settings. Tremor detection and frequency estimation was enabled using suitable radar signal preprocessing followed by a neural network comprising convolutional and recurrent layers. Reference frequencies were obtained from an inertial measurement unit (IMU) attached to the participants' right hands. Cross-validation revealed a mean absolute error (MAE) of 1.47 Hz in radar-based frequency estimation compared with the reference and a 90% accuracy in distinguishing the presence or absence of tremor, highlighting the proposed approach's high potential for future tremor assessment. © 2023 IEEE.

关键词： Frequency estimation

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Simple unsupervised keyphrase extraction using sentence embeddings

arXiv

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arXiv 2018年

作者： Bennani-Smires, Kamil Musat, Claudiu Hossmann, Andreaa Baeriswyl, Michael Jaggi, Martin Data Analytics & AI Swisscom AG Machine Learning and Optimization Laboratory EPFL

Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Supervised keyphrase extraction requires large amounts of labeled training data and generalizes very poorly outside the domain of the training data. At the same time, unsupervised systems have poor accuracy, and often do not generalize well, as they require the input document to belong to a larger corpus also given as input. Addressing these drawbacks, in this paper, we tackle keyphrase extraction from single documents with EmbedRank: a novel unsupervised method, that leverages sentence embeddings. EmbedRank achieves higher F-scores than graph-based state of the art systems on standard datasets and is suitable for real-time processing of large amounts of Web data. With EmbedRank, we also explicitly increase coverage and diversity among the selected keyphrases by introducing an embedding-based maximal marginal relevance (MMR) for new phrases. A user study including over 200 votes showed that, although reducing the phrases’ semantic overlap leads to no gains in F-score, our high diversity selection is preferred by humans. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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Exploration of Process Mining Opportunities In Educational Software Engineering - The GitLab Analyser 13

Exploration of Process Mining Opportunities In Educational S...

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13th International Conference on Educational data Mining, EDM 2020

作者： Dumbach, Philipp Aly, Alexander Zrenner, Markus Eskofier, Bjoern M. Machine Learning and Data Analytics Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Carl-Thiersch-Str. 2b Erlangen91052 Germany

ISBN: (纸本)9781733673617

The increasing complexity in software development leads to the necessity for a detailed data analysis. Literature illustrates a stronger research focus on Educational Process Mining (EPM) being applied to the fields of e-learning and professional training. In this work, the opportunities of Process Mining (PM) are further examined by the evaluation of software engineering (SE) courses. The methodology follows the five stages of the L* life cycle model for PM projects using data from software repositories. The event log data was analyzed with the PM tool Disco to examine the students’ work following an agile development process. The new tool GitLab Analyser supports supervisors to visualize educational processes and still extracts event logs for the further analysis and application of PM techniques. © 2020 Proceedings of the 13th International Conference on Educational data Mining, EDM 2020. All rights reserved.

关键词： data mining

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OculusGraphy: Description and Time Domain Analysis of Full-Field Electroretinograms database

OculusGraphy: Description and Time Domain Analysis of Full-F...

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2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023

作者： Albasu, Faisal B. Dey, Subhankar Dolganov, Anton Yu. Hamzaoui, Oussama El Mustafa, Wisam M. Zhdanov, Aleksei E. Eng. Sch. of Info. Technologies Telecommunications and Control Systems Ural Federal University Yekaterinburg Russia University of Erlangen-Nuremberg Machine Learning and Data Analytics Lab Erlangen Germany

ISBN: (纸本)9798350336054

Electroretinography is an established electrophysiological technique for clinical investigation of visual health function. The test involves the non-invasive assessment of the retinal function using light stimulation. This study presents a comprehensive database of five types of signals, including Scotopic 2.0 ERG response, Photopic 2.0 ERG response, Maximum 2.0 ERG response, Photopic 2.0 EGR Flicker response and Scotopic 2.0 ERG Oscillatory Potentials. The database includes 1975 signals from 323 patients, consisting of both adults and children. This study seeks to evaluate and analyze the classical parameters of three of the five signals, specifically Scotopic 2.0 ERG response, Photopic 2.0 ERG response, and Maximum 2.0 ERG response, using time-domain analysis. Correlation analysis was conducted in order to gain insights into the potential relationship between a patient's age and the classical parameters. However, the results suggest little to no correlation between these factors. The database presented in this study will serve as a useful resource for further research in the field of electrophysiology. Future research in this area could explore the use of other advanced analysis methods, such as frequency and time-frequency domain analyses, in order to gain further insights into the nuances of these signals and to better differentiate between healthy patients and those with pathologies. Additionally, further research is needed to establish norms for pediatric signals in order to better understand retinal health in children. © 2023 IEEE.

关键词： database systems

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Problems and Prospectives of Big data Storage and Processing Standartization

Problems and Prospectives of Big Data Storage and Processing...

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IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Evgeniy N. Pavlovskiy Stream Data Analytics and Machine Learning lab Novosibirsk State University Novosibirsk Russia

In the paper, we analyze the problem of standardization in the domain of storage and processing of big data in the application to the Internet of things. We highlight the underlying problems of big data; analyze the scientific, technological and economic barriers to the development of big data. We offer perspective research directions in the domain of development of standards of data representation.

关键词： Big data Internet of Things Standards Task analysis Neural networks data structures

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XAIMed-Net: Towards Explainable Brain Tumour Detection in 2D T1-weighted CE-MRI Images Using Transfer learning 26

XAIMed-Net: Towards Explainable Brain Tumour Detection in 2D...

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26th Irish machine Vision and Image Processing Conference, IMVIP 2024

作者： Adegboro, Oluwabukola Ganepola, Vayangi Dietlmeier, Julia Mazo, Claudia O'Connor, Noel E. SFI Centre for Research Training in Machine Learning Dublin City University Ireland Insight SFI Research Centre for Data Analytics Dublin City University Ireland School of Computing Dublin City University Ireland

ISBN: (纸本)9781837242672

Malignant brain tumours comprise one of the deadliest types of cancer and their timely and accurate detection is receiving urgent attention. Radiology can benefit from the recent impressive progress of AI and deep learning that provide the non-invasive diagnostic opportunity. However, the challenge of explainable AI-based decision-making processes remains. In this work, we aim to take a step toward more explainable classification of brain tumours in medical images by designing a new convolutional architecture while also leveraging the advantages of transfer learning. Specifically, we introduce a new shallow convolutional head that can be added to a pre-trained ResNet50 model to produce EigenCAM-based importance heatmaps. We investigate pre-training based on the ImageNet and RadImageNet datasets. Furthermore, we also address the problem of weakly-supervised brain tumour segmentation in a challenging 2D T1-weighted CE-MRI dataset by developing an approach to post-process the resulting heatmaps. Our preliminary experimental results with the Dice Score Coefficient of 0.145 and the new objective distance metric μΔ = 59.55 show the potential of combining large pre-trained models with shallow convolutional architectures to localize tumours using only weak classification labels. Our code is publicly available at https://***/juliadietlmeier/Explainable_brain_tumor_detection_in_MRI. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Transfer learning

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Impact of Distributed Training on Mask R-CNN Model Performance for Image Segmentation 1

Impact of Distributed Training on Mask R-CNN Model Performan...

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1st International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020

作者： Ranjit, Mercy Prasanna Ganapathy, Gopinath Manuel, Ranjit Frederick Microsoft Corporation India Ltd AI Machine Learning Bangalore India Bharathidasan University School of Computer Science and Engineering Trichy India ITC Infotech India Ltd Data Analytics AI Bangalore India

ISBN: (纸本)9781728141428

The paper compares and discusses the impact of distributed deep learning training using the Horovod framework on the performance metrics of image segmentation model trained using the Mask R-CNN (Region-based Convolutional Neural Network) architecture in comparison with the baseline metrics obtained from running the training on a single node. Image segmentation is a complex problem in comparison with image classification and the fluctuations introduced due to the distributed gradient computations while running the training distributed is less pronounced on simpler problems like image classification while it is more pronounced for image segmentation where we measure the performance at pixel level. It is important to know how the latest algorithms and distribution frameworks perform when training the model on distributed compute with parallel gradient calculations, adjusted learning rates and averaged neural network weight updates. The paper also aimed to understand if the effective batch size per training iteration while performing the training distributed did not negatively impact the ability of the model to generalize when increasing the batch size as indicated in the previous studies. The distributed training with Horovod was performed using the Azure machine learning service on scalable cloud compute cluster. The aim of the study was not comparison in-terms of the execution time which has been well established on previous studies but on understanding the impact on the quality of the predictions measured with standard validation metrics for image segmentation using the ISIC (International Skin Imaging Collaboration) 2018 Skin Lesion image dataset. © 2020 IEEE.

关键词： Image segmentation

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Predicting Influential Higher-Order Patterns in Temporal Network data 14

Predicting Influential Higher-Order Patterns in Temporal Net...

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14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

作者： Gote, Christoph Perri, Vincenzo Scholtes, Ingo Data Analytics Group University of Zurich Zurich Switzerland Eth Zurich Systems Design Switzerland Julius-Maximilians-Universität Würzburg Chair of Machine Learning for Complex Networks Würzburg Germany

ISBN: (纸本)9781665456616

Networks are frequently used to model complex systems comprised of interacting elements. While edges capture the topology of direct interactions, the true complexity of many systems originates from higher-order patterns in paths by which nodes can indirectly influence each other. Path data, representing ordered sequences of consecutive direct interactions, can be used to model these patterns. On the one hand, to avoid overfitting, such models should only consider those higher-order patterns for which the data provide sufficient statistical evidence. On the other hand, we hypothesise that network models, which capture only direct interactions, underfit higher-order patterns present in data. Consequently, both approaches are likely to misidentify influential nodes in complex networks. We contribute to this issue by proposing five centrality measures based on MOGen, a multi-order generative model that accounts for all indirect influences up to a maximum distance but disregards influences at higher distances. We compare MOGen-based centralities to equivalent measures for network models and path data in a prediction experiment where we aim to identify influential nodes in out-of-sample data. Our results show strong evidence supporting our hypothesis. MOGen consistently outperforms both the network model and path-based prediction. We further show that the performance difference between MOGen and the path-based approach disappears if we have sufficient observations, confirming that the error is due to overfitting. © 2022 IEEE.

关键词： Forecasting

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XAI-ResUNet: Analysing the Impact of Pre-training in ResUNet Architectures for Multiple Sclerosis Lesion Segmentation using EigenGradCAM 26

XAI-ResUNet: Analysing the Impact of Pre-training in ResUNet...

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26th Irish machine Vision and Image Processing Conference, IMVIP 2024

作者： Ganepola, Vayangi Mathur, Prateek Adegboro, Oluwabukola Dietlmeier, Julia Lawlor, Aonghus O'Connor, Noel E. Mazo, Claudia SFI Centre for Research Training in Machine Learning Dublin Ireland Insight SFI Research Centre for Data Analytics Dublin Ireland Dublin City University Dublin Ireland University College Dublin Dublin Ireland

ISBN: (纸本)9781837242672

Multiple Sclerosis (MS) is a chronic disease that causes lesions in the central nervous system. Diagnosing MS is a challenging process that strongly relies on Magnetic Resonance Imaging (MRI) and exhibits high and largely unexplained variability across patients. In this paper, we propose a novel XAI-ResUNet architecture by integrating ResNet-50 as an encoder in a U-Net-like architecture and demystifying the detection and segmentation process by incorporating EigenGradCAM into the encoder. We used the training and testing sets of the MSSEG-2016 dataset for our experiments. We compared three architectures with ResNet-50 pre-trained on ImageNet, RadImageNet, and without pre-training. Our preliminary results show that ResNet-50 pre-trained on ImageNet outperforms the others, achieving a DSC of 0.638. We also demonstrate how pre-training on large image datasets can affect MS lesion detection and segmentation. Our code is publicly available at https://***/VishmiVishara/XAI-ResUNet. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Magnetic resonance imaging

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