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An IoT Course Program to Foster the Adoption of IoT Driven Food and Agriculture in Sub-Saharan Africa (SSA)

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An IoT Course Program to Foster the Adoption of IoT Driven F...

2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Kuaban, Godlove Suila Nowak, Mateusz Czekalski, Piotr Tokarz, Krzysztof Tangka, Julius Kewir Siggursson, Kjartan Nikitenko, Agris Berkolds, Karlis Sell, Raivo Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice 44-100 Poland Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Renewable Energy Laboratory University of Dschang Dschang Cameroon University of Twente Netherlands Riga Technical University Latvia TalTech Estonia

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

The Internet of Things (IoT) technologies, together with other technological advances such as big data analytics, Artificial Intelligence (AI), automation, and unmanned electrical vehicles (e.g., self-driving vehicles and drones), are currently being used to transform the food and agriculture industry to increase yields significantly with minimal damage to the environment. These recent advances in Information and Communication Technologies and advances in biotechnology are driving the transition into the fourth agricultural revolution or agriculture 4.0. Some research studies on the adoption of IoT in the agriculture sector in Sub-Saharan Africa concluded that some of the reasons the rate of adoption of IoT in Africa is still very low are gaps in knowledge, skills, finance, and infrastructure. The knowledge and skills gaps are partly a result of an educational curriculum that is not up to date with current advances in the food and agricultural industry. To foster IoT adoption in the food and agriculture sectors and other sectors or industries in Sub-saharan Africa, we propose an IoT course program consisting of lectures and laboratory modules. The course is accompanied by textbooks, video lectures that are freely available on Edx, and remote online laboratories with the hardware resources hosted in some European countries but accessible through the Internet. These courses can also be adapted by vocational training schools, Higher Institutes, and universities in Sub-Saharan Africa or could be used by professionals and enthusiasts to improve their skills. © 2022 IEEE.

关键词： Agriculture

Persistent Homology with Improved Locality Information for more Effective Delineation

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arXiv 2021年

作者： Oner, Doruk Garin, Adélie Kozinski, Mateusz Hess, Kathryn Fua, Pascal The Computer Vision Laboratory Switzerland The Laboratory for Topology and Neuroscience École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland The Institute of Computer Graphics and Vision TU Graz Graz8010 Austria

Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results. However, existing methods are very global and ignore the location of topological features. In this paper, we remedy this by introducing a new filtration function that fuses two earlier approaches: thresholding-based filtration, previously used to train deep networks to segment medical images, and filtration with height functions, typically used to compare 2D and 3D shapes. We experimentally demonstrate that deep networks trained using our PH-based loss function yield reconstructions of road networks and neuronal processes that reflect ground-truth connectivity better than networks trained with existing loss functions based on PH. Code is available at https://***/doruk-oner/PH-TopoLoss. Copyright © 2021, The Authors. All rights reserved.

关键词： Topology

AIR-DA: Adversarial Image Reconstruction for Unsupervised Domain Adaptive Object Detection

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arXiv 2023年

作者： Sun, Kunyang Lin, Wei Shi, Haoqin Zhang, Zhengming Huang, Yongming Bischof, Horst The School of Information Science and Engineering The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Pervasive Communications Center Purple Mountain Laboratories Nanjing211111 China The Institute for Computer Graphics and Vision Graz University of Technology Graz8010 Austria

Unsupervised domain adaptive object detection is a challenging vision task where object detectors are adapted from a label-rich source domain to an unlabeled target domain. Recent advances prove the efficacy of the adversarial based domain alignment where the adversarial training between the feature extractor and domain discriminator results in domain-invariance in the feature space. However, due to the domain shift, domain discrimination, especially on low-level features, is an easy task. This results in an imbalance of the adversarial training between the domain discriminator and the feature extractor. In this work, we achieve a better domain alignment by introducing an auxiliary regularization task to improve the training balance. Specifically, we propose Adversarial Image Reconstruction (AIR) as the regularizer to facilitate the adversarial training of the feature extractor. We further design a multi-level feature alignment module to enhance the adaptation performance. Our evaluations across several datasets of challenging domain shifts demonstrate that the proposed method outperforms all previous methods, of both one- and two-stage, in most settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization

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arXiv 2021年

作者： Mirza, Muhammad Jehanzeb Micorek, Jakub Possegger, Horst Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Austria

Domain adaptation is crucial to adapt a learned model to new scenarios, such as domain shifts or changing data distributions. Current approaches usually require a large amount of labeled or unlabeled data from the shifted domain. This can be a hurdle in fields which require continuous dynamic adaptation or suffer from scarcity of data, e.g. autonomous driving in challenging weather conditions. To address this problem of continuous adaptation to distribution shifts, we propose Dynamic Unsupervised Adaptation (DUA). By continuously adapting the statistics of the batch normalization layers we modify the feature representations of the model. We show that by sequentially adapting a model with only a fraction of unlabeled data, a strong performance gain can be achieved. With even less than 1% of unlabeled data from the target domain, DUA already achieves competitive results to strong baselines. In addition, the computational overhead is minimal in contrast to previous approaches. Our approach is simple, yet effective and can be applied to any architecture which uses batch normalization. We show the utility of DUA by evaluating it on a variety of domain adaptation datasets and tasks including object recognition, digit recognition and object detection. © 2021, CC BY.

关键词： Object recognition

An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions

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arXiv 2022年

作者： Mirza, Muhammad Jehanzeb Masana, Marc Possegger, Horst Bischof, Horst Institute of Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Austria TU Graz SAL Dependable Embedded Systems Lab Silicon Austria Labs Austria

Although deep neural networks enable impressive visual perception performance for autonomous driving, their robustness to varying weather conditions still requires attention. When adapting these models for changed environments, such as different weather conditions, they are prone to forgetting previously learned information. This catastrophic forgetting is typically addressed via incremental learning approaches which usually re-train the model by either keeping a memory bank of training samples or keeping a copy of the entire model or model parameters for each scenario. While these approaches show impressive results, they can be prone to scalability issues and their applicability for autonomous driving in all weather conditions has not been shown. In this paper we propose DISC - Domain Incremental through Statistical Correction - a simple online zero-forgetting approach which can incrementally learn new tasks (i.e. weather conditions) without requiring re-training or expensive memory banks. The only information we store for each task are the statistical parameters as we categorize each domain by the change in first and second order statistics. Thus, as each task arrives, we simply 'plug and play' the statistical vectors for the corresponding task into the model and it immediately starts to perform well on that task. We show the efficacy of our approach by testing it for object detection in a challenging domain-incremental autonomous driving scenario where we encounter different adverse weather conditions, such as heavy rain, fog, and snow. © 2022, CC BY.

关键词： Meteorology

A Novel Approach for User Demand-aware Data Center Construction and Service Consolidation 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Lv, Yifei Guo, Kunyin Xia, Yunni Li, Yin Ma, Yong Li, Fan Zeng, Linchengxi Peng, Qinglan Long, Tingyan College of Computer Science Chongqing University Chongqing400030 China Guangzhou Institute of Software Application Technology Guangzhou510000 China School of Computers and Information Jiangxi Normal University Nanchang330022 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610000 China Chongqing China School of Artificial Intelligence Henan University Kaifeng475001 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers (DCs) to handle business, monetary, Internet and commercial applications of different enterprises. A cloud data center encompasses thousands of physical server nodes arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms. More and more cloud data centers are turning for adapting to dynamics of user demands and reducing operational cost. Therefore, in this paper, we propose a user demand-aware (UDA) method for servers selection and a modified adaptive large neighbourhood search (MALNS) algorithm for dynamic service consolidation. Experiments based on real-world datasets demonstrate our approach outperformed conventional strategies in terms of multiple metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Heuristic algorithms

Robustness of object detectors in degrading weather conditions

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arXiv 2021年

作者： Mirza, Muhammad Jehanzeb Buerkle, Cornelius Jarquin, Julio Opitz, Michael Oboril, Fabian Scholl, Kay-Ulrich Bischof, Horst Christian Doppler Laboratory for Embedded Machine Learning Institute for Computer Graphics and Vision Graz University of Technology Austria Intel Labs Karlsruhe Germany

State-of-the-art object detection systems for autonomous driving achieve promising results in clear weather conditions. However, such autonomous safety critical systems also need to work in degrading weather conditions, such as rain, fog and snow. Unfortunately, most approaches evaluate only on the KITTI dataset, which consists only of clear weather scenes. In this paper we address this issue and perform one of the most detailed evaluation on single and dual modality architectures on data captured in real weather conditions. We analyse the performance degradation of these architectures in degrading weather conditions. We demonstrate that an object detection architecture performing good in clear weather might not be able to handle degrading weather conditions. We also perform ablation studies on the dual modality architectures and show their limitations. © 2021, CC BY.

关键词： Object detection

How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation

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arXiv 2024年

作者： Ferreira, André Solak, Naida Li, Jianning Dammann, Philipp Kleesiek, Jens Alves, Victor Egger, Jan Center ALGORITMI/LASI University of Minho Braga4710-057 Portugal Computer Algorithms for Medicine Laboratory Graz Austria University Medicine Essen Girardetstraße 2 Essen45131 Germany University Medicine Essen Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria Department of Neurosurgery and Spine Surgery University Hospital Essen Essen Germany

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to massively increase the amount of available samples for training three different deep learning models for brain tumour segmentation, the first task of the BraTS2023 challenge. The first model is the standard nnU-Net, the second is the Swin UNETR and the third is the winning solution of the BraTS 2021 Challenge. The entire pipeline is built on the nnU-Net implementation, except for the generation of the synthetic data. The use of convolutional algorithms and transformers is able to fill each other's knowledge gaps. Using the new metric, our best solution achieves the dice results 0.9005, 0.8673, 0.8509 and HD95 14.940, 14.467, 17.699 (whole tumour, tumour core and enhancing tumour) in the validation set. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing