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7th World Symposium on Communication engineering, WSCE 2024

作者： Michoulis, George Tsourdinis, Kyros Niotis, George Kyranou, Konstantinos Fountoukidis, Eleutherios Lagkas, Thomas Argyriou, Vasileios Sarigiannidis, Panagiotis Psannis, Konstantinos E. Goudos, Sotirios K. Sidroco Holdings Ltd R&D Dpt. Thessaloniki Greece Sidroco Holdings Ltd R&D Dpt. Nicosia Cyprus Democritus University of Thrace Dpt. of Computer Science Kavala Greece University of Western Macedonia Dpt. of Electrical & Computer Engineering Kozani Greece Kingston University London Kingston Upon Thames Dpt. of Networks and Digital Media Surrey United Kingdom University of Macedonia Department of Applied Informatics Thessaloniki Greece ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9798331542825

This paper presents FraMark, a blockchain-based framework for 5G network management that utilizes Hyperledger Fabric and fractional Non-Fungible Tokens (NFTs) to optimize resource allocation. We introduce a comprehensive system that fractionalizes digital assets to represent 5G network resources, enhancing efficiency and allocation flexibility. Our framework addresses the challenges of 5G network management by ensuring low latency, high transaction throughput, and secure resource trading. We conduct experiments to evaluate the performance of our approach, including CPU and memory utilization, latency analysis, and block size assessment. The results demonstrate the effectiveness of FraMark in improving network resource utilization and transaction efficiency, validating our framework's claims. By leveraging blockchain technology and innovative resource slicing techniques, FraMark paves the way for dynamic and efficient 5G network management. © 2024 IEEE.

关键词： 5G mobile communication systems

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Optimizing Federated Learning through Lightweight and Scalable Blockchain

Optimizing Federated Learning through Lightweight and Scalab...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Georgios Andronikidis George Niotis Charis Eleftheriadis Konstantinos Kyranou Sotiris Nikoletseas Panagiotis Sarigiannidis Sidroco Holdings Ltd Nicosia Cyprus Dpt. of Computer Engineering & Informatics University of Patras Patras Greece Dpt. of Electrical & Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

In recent years, the increased focus on preserving data privacy, the advancements in machine learning, and the ongoing innovations in blockchain technology have sparked significant interest in implementing blockchain-based federated learning frameworks. Proposed solutions primarily focus on addressing the growing size of the blockchain in relation to machine learning algorithms, particularly the number of parameters involved in federated learning. However, the expanding blockchain scale results in increased time requirements, making many solutions impractical. In light of these issues, our research introduces a proof-of-concept for a lightweight, scalable, and modular blockchain network using Hyperledger Fabric to enhance federated learning. Performance evaluations using Hyperledger Caliper show that the proposed blockchain network effectively improves federated learning without compromising latency and throughput. Experimental results affirm blockchain’s proficiency in managing substantial transaction payloads from federated learning clients, especially with resource-intensive machine learning algorithms during training.

关键词： Training Machine learning algorithms Federated learning Distributed ledger computer architecture Throughput Fabrics

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Question Answering Chatbots for Biomedical Research Using Transformers

Question Answering Chatbots for Biomedical Research Using Tr...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Evdokia Xygi Andreas D. Andriopoulos Dimitrios A. Koutsomitropoulos Computer Engineering and Informatics Dpt. University of Patras Patras Greece

Professionals as well as the general public need effective help to access, understand and consume complex biomedical concepts. The existence of an interaction environment capable of automatically processing such information - thus replacing human intervention - such as chatbots, is however challenging. In this paper we propose a method of utilizing chatbots in the domain of biomedicine. In the implementation we choose to incorporate the BERT algorithm, so as to adopt a modern technique for natural language processing tasks. We use several pre-trained models (RoBERTa, XLM-R, BERT Large, and BioBert) in order to evaluate their ability to back the chatbot infrastructure. The data is retrieved from the PubMed repository, with the final set being formed into full sentences or potential chatbot responses, thus preserving their conceptual meaning. Response selection is performed using similarity metrics and F-score. The results create a ranking of the models placing related ones closely, recognizing the ability to always answer each question and highlighting the importance of the training previously applied to them. These are compared to the Count Vectorizer technique, which appears to perform better, but with several weaknesses, as many questions could not be answered.

关键词： Training Measurement Biological system modeling Bit error rate Chatbots Transformers Question answering (information retrieval)

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Validating Ontology-based Annotations of Biomedical Resources using Zero-shot Learning 2021

Validating Ontology-based Annotations of Biomedical Resource...

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12th International Conference on Computational Systems-Biology and Bioinformatics, CSBio 2021

作者： Koutsomitropoulos, Dimitrios Computer Engineering and Informatics Dpt. University of Patras Patras26500 Greece

ISBN: (纸本)9781450385107

Authoritative thesauri in the form of web ontologies offer a sound representation of domain knowledge and can act as a reference point for automated semantic tagging. On the other hand, current language models achieve to capture contextualized semantics of text corpora and can be leveraged towards this goal. We present an approach for injecting subject annotations using query term expansion against such ontologies in the biomedical domain. For the user to have an indication of the usefulness of these suggestions we further propose an online method for validating the quality of annotations using NLI models such as BART and XLM-R. To circumvent training barriers posed by very large label sets and scarcity of data we rely on zero-shot classification and show that semantic matching can contribute above-average thematic annotations. Also, a web-based validation service can be attractive for human curators vs. the overhead of pretraining large, domain-tailored classification models. © 2021 ACM.

关键词： Thesauri

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FraMark: A Blockchain Marketplace for a 5G Network Management using Fractional NFTs

FraMark: A Blockchain Marketplace for a 5G Network Managemen...

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IEEE World Symposium on Communication engineering (WSCE)

作者： George Michoulis Kyros Tsourdinis George Niotis Konstantinos Kyranou Eleutherios Fountoukidis Thomas Lagkas Vasileios Argyriou Panagiotis Sarigiannidis Konstantinos E. Psannis Sotirios K. Goudos R&D Dpt. Sidroco Holdings Ltd Thessaloniki Greece R&D Dpt. Sidroco Holdings Ltd Nicosia Cyprus Dpt. of Computer Science Democritus University of Thrace Kavala Greece Dpt. of Electrical & Computer Engineering University of Western Macedonia Kozani Greece Dpt. of Networks and Digital Media Kingston University London Kingston Upon Thames Surrey UK Department of Applied Informatics University of Macedonia Thessaloniki Greece ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798331542825

ISBN: (纸本)9798331542832

This paper presents FraMark, a blockchain-based framework for 5G network management that utilizes Hyperledger Fabric and fractional Non-Fungible Tokens (NFTs) to optimize resource allocation. We introduce a comprehensive system that fractionalizes digital assets to represent 5G network resources, enhancing efficiency and allocation flexibility. Our framework addresses the challenges of 5G network management by ensuring low latency, high transaction throughput, and secure resource trading. We conduct experiments to evaluate the performance of our approach, including CPU and memory utilization, latency analysis, and block size assessment. The results demonstrate the effectiveness of FraMark in improving network resource utilization and transaction efficiency, validating our framework’s claims. By leveraging blockchain technology and innovative resource slicing techniques, FraMark paves the way for dynamic and efficient 5G network management.

关键词： 5G mobile communication Distributed ledger Throughput Dynamic scheduling Fabrics Resource management Nonfungible tokens Low latency communication

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A Multispectral Dataset for the Detection of Tutaabsoluta and Leveillula Taurica in Tomato Plants

SSRN

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SSRN 2022年

作者： Georgantopoulos, Panagiotis S. Papadimitriou, Dimitris Constantinopoulos, Constantinos Manios, Thrasyvoulos Daliakopoulos, Ioannis N. Kosmopoulos, Dimitrios Computer Engineering & Informatics Dpt. University of Patras Rio26504 Greece Dpt. of Agriculture Hellenic Mediterranean University Heraklion71410 Greece

Tomato (Solanum lycopersicum) is one of the most importantvegetables for human nutrition and its cultivation employs amounts of resourcesworldwide. However, tomato cultivation is plagued by several diseases and peststhat increase the production cost and introduce additional environmental andhealth risks due to pesticide use. Timely disease and pest detection is of highimportance for tomato crop production and the environment, as plant protec-tion input can be optimized. Here, we present a dataset of multispectral images(RGB and NIR) of tomato plants, at various stages of infection with Tuta ab-soluta and Leveillula taurica, which to our knowledge is unique. The datasetcomprised of 263 images collected from a real greenhouse. Additionally, weapplied a baseline Faster-RCNN object detector for the localization and classi-fication of lesions. Our experiments include (i) a version for the RGB channelsand (ii) a custom backbone architecture version for feature fusion using thesame Faster-RCNN head. Results of mAP 18.5% and mAP 20.2% respectively,highlight the added value of NIR spectral bands for detecting these diseases. © 2022, The Authors. All rights reserved.

关键词： Cultivation

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A Multispectral Dataset for the Detection of Tuta Αbsoluta and Leveillula Τaurica in Tomato Plants

SSRN

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SSRN 2022年

作者： Georgantopoulos, Panagiotis S. Papadimitriou, Dimitris Constantinopoulos, Constantinos Manios, Thrasyvoulos Daliakopoulos, Ioannis Kosmopoulos, Dimitrios Computer Engineering & Informatics Dpt. University of Patras Rio26504 Greece Dpt. of Agriculture Hellenic Mediterranean University Heraklion71410 Greece

Tomato ( Solanum lycopersicum ) is one of the most important vegetables for human nutrition and its cultivation employs amounts of resources worldwide. However, tomato cultivation is plagued by several diseases and pests that increase production cost and introduce additional environmental and health risks due to pesticide use. Timely disease and pest detection is of high importance for tomato crop output and the environment, since plant protection input can be optimized. Here, we present a dataset of multispectral images (RGB and NIR) of tomato plants, at various stages of infection with Tuta absoluta and Leveillula taurica , which to our knowledge is unique. The dataset comprised of 263 images collected from a real greenhouse. Additionally, we applied a baseline Faster-RCNN object detector for the localization and classification lesions. Our experiments include (i) a version for the RGB channels and (ii) a custom backbone architecture version for feature fusion using the same Faster-RCNN head. Results of mAP 18.5% and mAP 20.2% respectively, highlight the added value of NIR spectral bands for detecting these diseases. © 2022, The Authors. All rights reserved.

关键词： Fruits

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Self-supervised feature learning of 1D convolutional neural networks with contrastive loss for eating detection using an in-ear microphone

arXiv

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arXiv 2021年

作者： Papapanagiotou, Vasileios Diou, Christos Delopoulos, Anastasios The Multimedia Understanding Group Dpt. of Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki Greece Department of Informatics and Telematics Harokopio University of Athens Greece

The importance of automated and objective monitoring of dietary behavior is becoming increasingly accepted. The advancements in sensor technology along with recent achievements in machine-learning-based signal-processing algorithms have enabled the development of dietary monitoring solutions that yield highly accurate results. A common bottleneck for developing and training machine learning algorithms is obtaining labeled data for training supervised algorithms, and in particular ground truth annotations. Manual ground truth annotation is laborious, cumbersome, can sometimes introduce errors, and is sometimes impossible in free-living data collection. As a result, there is a need to decrease the labeled data required for training. Additionally, unlabeled data, gathered in-the-wild from existing wearables (such as Bluetooth earbuds) can be used to train and fine-tune eating-detection models. In this work, we focus on training a feature extractor for audio signals captured by an in-ear microphone for the task of eating detection in a self-supervised way. We base our approach on the SimCLR method for image classification, proposed by Chen et al. from the domain of computer vision. Results are promising as our self-supervised method achieves similar results to supervised training alternatives, and its overall effectiveness is comparable to current state-of-the-art methods. Code is available at https://***/mug-auth/ssl-chewing. Copyright © 2021, The Authors. All rights reserved.

关键词： Microphones

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Subject Classification of Learning Resources Using Word Embeddings and Semantic Thesauri

Subject Classification of Learning Resources Using Word Embe...

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2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019

作者： Koutsomitropoulos, Dimitrios A. Andriopoulos, Andreas D. Likothanassis, Spiridon D. Computer Engineering and Informatics Dpt. University of Patras Patras Greece

ISBN: (纸本)9781728118628

Open Educational Resources (OERs) are often scattered among various sources and may follow different metadata schemata. In addition, they may not include exhaustive annotations;even worse, their subject characterization, if any, may be represented by arbitrary, ad-hoc keywords instead of standard, controlled vocabularies, a fact that stretches up the search space and hampers interoperability. To address this issue, in this paper we propose a twofold method based on two seemingly disjoint technology stacks: machine learning and the semantic web. First, OERs harvested from various repositories are assigned subject terms from a formal, standard thesaurus for a domain of interest, by discovering the semantic matches of the harvesting keyword within the thesaurus ontology. Then, we use word embeddings to represent an item's metadata and compute its similarity with the thesaurus keywords. These word embeddings are learned by a doc2vec model that has been trained with already annotated corpora from the biomedical domain. By combining both worlds, we show that it is possible to produce a reasonable set of thematic suggestions which exceed a certain similarity threshold. © 2019 IEEE.

关键词： Ontology

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Recognition of food-texture attributes using an in-ear microphone

arXiv

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arXiv 2021年

作者： Mars, Monica Diou, Christos Papapanagiotou, Vasileios van den Boer, Janet Delopoulos, Anastasios Multimedia Understanding Group Dpt. Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki Greece Department of Informatics and Telematics Harokopio University of Athens Greece Dpt. of Biomedical Signals and Systems Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Division of Human Nutrition and Health Wageningen University Netherlands

Food texture is a complex property;various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applications in multiple fields, including health sciences and food engineering. In this work we use an in-ear microphone, commonly used for chewing detection, and propose algorithms for recognizing three food-texture attributes, specifically crispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each attribute, and propose two algorithms: one that recognizes each texture attribute at the chew level and one at the chewing-bout level. We evaluate the proposed algorithms using leave-one-subject-out cross-validation on a dataset with 9 subjects. We also evaluate them using leave-one-food-type-out cross-validation, in order to examine the generalization of our approach to new, unknown food types. Our approach performs very well in recognizing crispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness. Copyright © 2021, The Authors. All rights reserved.

关键词： Wearable technology

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