| 1. |
- Hagos, Desta Haileselassie, et al.
(author)
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ExtremeEarth Meets Satellite Data From Space
- 2021
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In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1939-1404 .- 2151-1535. ; 14, s. 9038-9063
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Journal article (peer-reviewed)abstract
- Bringing together a number of cutting-edge technologies that range from storing extremely large volumes of data all the way to developing scalable machine learning and deep learning algorithms in a distributed manner and having them operate over the same infrastructure poses unprecedented challenges. One of these challenges is the integration of European Space Agency (ESA)'s Thematic Exploitation Platforms (TEPs) and data information access service platforms with a data platform, namely Hopsworks, which enables scalable data processing, machine learning, and deep learning on Copernicus data, and development of very large training datasets for deep learning architectures targeting the classification of Sentinel images. In this article, we present the software architecture of ExtremeEarth that aims at the development of scalable deep learning and geospatial analytics techniques for processing and analyzing petabytes of Copernicus data. The ExtremeEarth software infrastructure seamlessly integrates existing and novel software platforms and tools for storing, accessing, processing, analyzing, and visualizing large amounts of Copernicus data. New techniques in the areas of remote sensing and artificial intelligence with an emphasis on deep learning are developed. These techniques and corresponding software presented in this article are to be integrated with and used in two ESA TEPs, namely Polar and Food Security TEPs. Furthermore, we present the integration of Hopsworks with the Polar and Food Security use cases and the flow of events for the products offered through the TEPs.
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| 2. |
- Hagos, Desta Haileselassie, et al.
(author)
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Scalable Artificial Intelligence for Earth Observation Data Using Hopsworks
- 2022
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In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:8
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Journal article (peer-reviewed)abstract
- This paper introduces the Hopsworks platform to the entire Earth Observation (EO) data community and the Copernicus programme. Hopsworks is a scalable data-intensive open-source Artificial Intelligence (AI) platform that was jointly developed by Logical Clocks and the KTH Royal Institute of Technology for building end-to-end Machine Learning (ML)/Deep Learning (DL) pipelines for EO data. It provides the full stack of services needed to manage the entire life cycle of data in ML. In particular, Hopsworks supports the development of horizontally scalable DL applications in notebooks and the operation of workflows to support those applications, including parallel data processing, model training, and model deployment at scale. To the best of our knowledge, this is the first work that demonstrates the services and features of the Hopsworks platform, which provide users with the means to build scalable end-to-end ML/DL pipelines for EO data, as well as support for the discovery and search for EO metadata. This paper serves as a demonstration and walkthrough of the stages of building a production-level model that includes data ingestion, data preparation, feature extraction, model training, model serving, and monitoring. To this end, we provide a practical example that demonstrates the aforementioned stages with real-world EO data and includes source code that implements the functionality of the platform. We also perform an experimental evaluation of two frameworks built on top of Hopsworks, namely Maggy and AutoAblation. We show that using Maggy for hyperparameter tuning results in roughly half the wall-clock time required to execute the same number of hyperparameter tuning trials using Spark while providing linear scalability as more workers are added. Furthermore, we demonstrate how AutoAblation facilitates the definition of ablation studies and enables the asynchronous parallel execution of ablation trials.
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| 3. |
- Idowu, Samuel, et al.
(author)
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NexTrend: Context-aware music-relay corridors using NFC tags
- 2013
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In: 7th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. - Piscataway, NJ : IEEE Computer Society Press. - 9780769549743 ; , s. 573-578
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Conference paper (peer-reviewed)abstract
- The rise of pervasive computing presents unique opportunities due to increasing availability of smart devices such as mobile phones and tablets equipped with various sensors enabling Near Field Communication (NFC) technologies. The growth of mobile computing has led to an increase in access to digital music. With the growth of digital music, the development of music information sharing services for users becomes important. The existing sharing methods are based on the users’ social network and preferences in music. However, sometimes, sharing music according to location and time is needed.This paper presents work on smart spaces equipped with NFC tags, deployed at different locations in hallways for discovering and sharing new music experiences. This concept provides a new way of interaction between passers-by for discovering music in relation to location. For example, the hallway locations use sensing devices to provide an automatic means of exchanging music information among the passers-by.We utilized NFC tags as Music-Relay hot spots. The hot spot retrieves information about the music a user is playing on her/his device while s/he is passing by the hot spot. The work contributes to a pervasive service that equips an environment with music context intelligence about a passer-bys choice of music and allows users to feel the musical presence of other users who have been in the same location at previous point in time. In general, this paper proposes a new music information sharing service using the music information captured from users at a specific location in time.
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| 4. |
- Rauniyar, Ashish, et al.
(author)
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COROID : A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics
- 2022
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In: 2022 IEEE 12TH SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP (SAM). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 216-220
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Conference paper (peer-reviewed)abstract
- Due to the current COVID-19 virus, which has already been declared a pandemic by the World Health Organization (WHO), we are witnessing the greatest pandemic of the decade. Millions of people are being infected, resulting in thousands of deaths every day across the globe. Even the world's best healthcare-providing countries could not handle the pandemic because of the strain of treating thousands of patients at a time. The count of infections and deaths is increasing at an alarming rate because of the spread of the virus. We believe that innovative technologies could help reduce pandemics to a certain extent until we find a definite solution from the medical field to handle and treat such pandemic situations. Technology innovation has the potential to introduce new technologies that could support people and society during these difficult times. Therefore, this paper proposes the idea of using drones as a companion to tackle current and future pandemics. Our COROID drone is based on the principle of crowdsourcing sensors data of the public's smart devices, which can correlate the reading of the infrared cameras equipped on COROID drones. To the best of our knowledge, this concept has yet to be investigated either as a concept or as a product. Therefore, we believe that the COROID drone is innovative and has a huge potential to tackle COVID-19 and future pandemics.
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| 5. |
- Rauniyar, Ashish, et al.
(author)
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Federated learning for medical applications : A taxonomy, current trends, challenges, and future research directions
- 2022
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Other publication (other academic/artistic)abstract
- With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)/Deep Learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. Consequently, the realm of data-driven medical applications has garnered significant attention spanning academia and industry, ushering in marked enhancements in healthcare delivery quality. Despite these strides, the adoption of AI-driven medical applications remains hindered by formidable challenges, including the arduous task of meeting security, privacy, and quality of service (QoS) standards. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. We delve into the contemporary research trends and their outcomes, unraveling the intricacies of designing reliable and scalable FL models. Our survey outlines the foundational statistical predicaments of FL, confronts device-related obstacles, delves into security challenges, and navigates the intricate terrain of privacy concerns, all while spotlighting its transformative potential within the medical domain. A primary focus of our study rests on medical applications, where we underscore the weighty burden of global cancer and illuminate the potency of FL in engendering computer-aided diagnosis tools that address this challenge with heightened efficacy. Further augmenting our discourse, recent literature has unveiled the inherent robustness and generalization of FL models compared to traditional data-driven medical applications. We hope that this review endeavors to serve as a checkpoint that sets forth the existing state-of-the-art works in a thorough manner and offers open problems and future research directions for this field.
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| 6. |
- Rauniyar, Ashish, et al.
(author)
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Federated Learning for Medical Applications : A Taxonomy, Current Trends, Challenges, and Future Research Directions
- 2024
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 11:5, s. 7374-7398
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Journal article (peer-reviewed)abstract
- With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and Quality-of-Service (QoS) standards. Recent developments in federated learning (FL) have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this article, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unraveling the complexities of designing reliable and scalable FL models. This article outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state of the art and identifying open problems and future research directions.
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| 7. |
- Wang, Tianze, et al.
(author)
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Accelerate Model Parallel Deep Learning Training Using Effective Graph Traversal Order in Device Placement
- 2022
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In: Distributed Applications and Interoperable Systems (DAIS 2022). - Cham : Springer Nature. ; , s. 114-130
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Conference paper (peer-reviewed)abstract
- Modern neural networks require long training to reach decent performance on massive datasets. One common approach to speed up training is model parallelization, where large neural networks are split across multiple devices. However, different device placements of the same neural network lead to different training times. Most of the existing device placement solutions treat the problem as sequential decisionmaking by traversing neural network graphs and assigning their neurons to different devices. This work studies the impact of neural network graph traversal orders on device placement. In particular, we empirically study how different graph traversal orders of neural networks lead to different device placements, which in turn affects the training time of the neural network. Our experiment results show that the best graph traversal order depends on the type of neural networks and their computation graphs features. In this work, we also provide recommendations on choosing effective graph traversal orders in device placement for various neural network families to improve the training time in model parallelization.
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