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作者： Kostamis, Periklis Sendros, Andreas Efraimidis, Pavlos S. Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67100 Greece

The cost of interacting with a blockchain and the time required to search and retrieve information from it are important factors in the design of Decentralized Applications (DApps). The corresponding approaches for data management in Ethereum vary from pure on-chain solutions to hybrid architectures. In this work, we thoroughly examine a wide range of data management methods, including unconventional ones, and evaluate them in terms of storage cost and retrieval latency while considering the impact of recent hard forks. More precisely, we elaborate on the use of Smart Contract (SC) storage as a data store for DApps and in view of the prohibitive cost inherent to this approach, we present and compare methods for low-cost data handling in Ethereum, namely the event-logs, the transaction payload, and the almost surprising exploitation of unused function arguments. In addition, we study hybrid schemes by using IPFS and Swarm as storage platforms and Ethereum as a timestamping proof mechanism. Such schemes are especially effective when large chunks of data have to be managed. The upload and retrieval performance of these platforms and the cost associated with storing (on-chain) the identifiers of the uploaded data are of utmost importance and therefore are investigated in detail. © 2023

关键词： Smart contract

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HFabD+M: A Web-based Platform for Automated Hyperledger Fabric Deployment and Management 1

HFabD+M: A Web-based Platform for Automated Hyperledger Fabr...

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1st IEEE Global Emerging Technology Blockchain Forum: Blockchain and Beyond, iGETblockchain 2022

作者： Zikos, Ioannis Sendros, Andreas Drosatos, George Efraimidis, Pavlos S. Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi67100 Greece Athena Research Center Institute for Language and Speech Processing Xanthi67100 Greece

ISBN: (纸本)9781665451987

Hyperledger Fabric is an open-source private permissioned blockchain that supports the use of smart contracts (chaincode). It is aimed mainly at private networks of companies. To serve the different needs of each company and to be flexible in customer requirements, it consists of various adaptive components. Although this structure efficiently addresses a wide range of needs, deploying such a network for research purposes or rapid development is complex. In this paper, we present a web-based system architecture for the automated deployment of a Hyperledger Fabric network, and in addition, we describe the tools needed to manage and update such a network. Finally, as a proof-of-concept, we implement the proposed architecture to demonstrate the feasibility of our approach. © 2022 IEEE.

关键词： Blockchain

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Eye movement patterns are similar during accurate multiple-target tracking 15

Eye movement patterns are similar during accurate multiple-t...

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15th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2024

作者： Bagha, Kamyar Kamkar, Shiva Moghaddam, Hamid Abrishami Oksama, Lauri Li, Jie Hyönä, Jukka Computer Engineering Department Khatam University Tehran Iran Laboratory Faculty of Electrical Engineering K.N.Toosi University of Technology Tehran Iran Tehran Iran Department of Psychology and Speech-Language Pathology University of Turku Turku Finland Center for Cognition and Brain Disorders Hangzhou Normal University Hangzhou China

ISBN: (纸本)9798350378245

Understanding how the brain works is a base of cognitive info-communication. To this aim we focus on multiple target tracking (MTT) as a key task that involves two important cognitive factors, attention and memory. Humans track multiple objects in their daily life while facing various challenges including occlusion and set-size. Eye movement research has shown that there are within and between subjects’ differences in scanpaths while performing MTT tasks. However, it is unclear if there is a winning scan pattern that would lead to a successful tracking of targets. To answer this question, we used dynamic time warping to compare the similarities between subjects’ scan patterns during an MTT task with different challenges. We studied the effect of set-size, occlusion, and trial response on the similarities. Then a mixed effect analysis was applied on the output to measure whether the findings were statistically significant. Results demonstrated that scan patterns were more similar when MTT task was performed correctly. It suggests that there is a common tracking strategy adopted by the viewers that leads to a correct response. Decoding this strategy has countless applications in the fields including human-computer interaction, brain-modeling and cognitive info-communication. © 2024 IEEE.

关键词： Eye movements

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Greek Sign language Recognition for the SL-ReDu Learning Platform 7

Greek Sign Language Recognition for the SL-ReDu Learning Pla...

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7th Workshop on Sign language Translation and Avatar Technology: The Junction of the Visual and the Textual Challenges and Perspectives, SLTAT 2022

作者： Papadimitriou, Katerina Potamianos, Gerasimos Sapountzaki, Galini Goulas, Theodor Efthimiou, Eleni Fotinea, Stavroula-Evita Maragos, Petros Department of Electrical & Computer Engineering University of Thessaly Volos Greece Department of Special Education University of Thessaly Volos Greece Institute for Language & Speech Processing Athena Research & Innovation Center Athens Greece School of Electrical & Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9791095546825

There has been increasing interest lately in developing education tools for sign language (SL) learning that enable self-assessment and objective evaluation of learners' SL productions, assisting both students and their instructors. Crucially, such tools require the automatic recognition of SL videos, while operating in a signer-independent fashion and under realistic recording conditions. Here, we present an early version of a Greek Sign language (GSL) recognizer that satisfies the above requirements, and integrate it within the SL-ReDu learning platform that constitutes a first in GSL with recognition functionality. We develop the recognition module incorporating state-of-the-art deep-learning based visual detection, feature extraction, and classification, designing it to accommodate a medium-size vocabulary of isolated signs and continuously fingerspelled letter sequences. We train the module on a specifically recorded GSL corpus of multiple signers by a web-cam in non-studio conditions, and conduct both multi-signer and signer-independent recognition experiments, reporting high accuracies. Finally, we let student users evaluate the learning platform during GSL production exercises, reporting very satisfactory objective and subjective assessments based on recognition performance and collected questionnaires, respectively. © European language Resources Association (ELRA), licensed under CC-BY-NC 4.0.

关键词： Deep learning

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Decoding speech Categorization using Microstate Cortical EEG Signals and Machine Learning

Decoding Speech Categorization using Microstate Cortical EEG...

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2024 IEEE Signal processing in Medicine and Biology Symposium, SPMB 2024

作者： Mahmud, M. Hasan, M. Yeasin, M. Bidelman, G. Univ. Of Tennessee Health Science Center Div. Of General Internal Medicine MemphisTN United States Middle Tennessee State University Computational And Data Science MurfreesboroTN United States University Of Memphis Department Of Electrical And Computer Engineering TN United States Indiana University Language And Hearing Sciences Department Of Speech BloomingtonIN United States

ISBN: (纸本)9798350388572

Categorical perception (CP) is a perceptual phenomenon that refers to the tendency of humans to group speech sounds into discrete units. In this work, we used cortical event-related potential (ERP) signals, recorded during a speech identification task, as input to a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and machine learning (ML) classifiers to examine how neural signatures of the brain distinguish prototypical vs. ambiguous speech token categories. Particularly, we used extreme gradient boosting (XGBoost), support vector machine, and random forest classifiers. Our analysis shows that using source reconstructed whole-brain data, the XGBoost classifier yielded the best classification accuracy of 94.12% with (area under the curve (AUC) 94.13%, Precision 94.00%, F1-score 94.00%) in the 197-258 ms time window after the stimulus onset. We also identified 15 important brain regions that distinguished vowel classes with accuracy of 90.28% (AUC 90.17%, F1-score 90.00%, Precision 90.00%), 4% less than whole-brain data. Remarkably, out of these 15 critical brain regions, nine were from the left hemisphere, consistent with the left-brain dominance for language processing. The best speech decoding was obtained from early portions of the ERP (~180-320 ms), which corroborates notions that sensory encoding is critical for successful phonetic processing. © 2024 IEEE.

关键词： Hidden Markov models

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Concurrent validity of instrumented insoles measuring gait and balance metrics in Parkinson's disease 46

Concurrent validity of instrumented insoles measuring gait a...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Watkinson, Sophia A. Anderson, Anthony Caiola, Michael Eguren, David Gonzalez, Michael Velazquez, Laureano Moro Dehak, Najim Motley, Chelsea Moukheiber, Emile Mills, Kelly Muir, Brittney Butala, Ankur Kontson, Kimberly Us Food and Drug Administration Division of Biomedical Physics Silver SpringMD United States Johns Hopkins University Center for Language and Speech Processing BaltimoreMD United States Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD United States Johns Hopkins University School of Medicine Department of Neurology BaltimoreMD United States Center for Limb Loss and Mobility Department of Veterans Affairsy SeattleWA United States

ISBN: (纸本)9798350371499

Several commercially available instrumented insole systems have been examined for validity and repeatability, but very few studies have focused on validation of the Moticon OpenGo sensor insoles in measuring gait and balance parameters in a clinical population. Given the paucity of studies examining the validity of these novel technologies in PD, there were two main goals of this research: (1) assess the concurrent validity of the Moticon OpenGo sensor insoles for gait and balance assessment in people with PD using a pressure-sensitive electronic walkway (Protokinetics Zeno™ walkway) as a reference system and (2) compare the gait metrics derived from the insole and walkway systems during a walking and turning task in order to assess the output of the systems under more real-world conditions. Twelve participants (5F/7M;mean age 71.3 ± 6.8 years) with a diagnosis of Parkinson's disease and a score of 2.5 or 3 on the Modified Hoehn & Yahr score performing straight-line walking, walking with turning, and balance tasks were included in this analysis. Differences in the estimated gait metrics from each system were evaluated through Bland-Altman analysis and calculation of Pearson's correlation coefficient (r). Results showed strong agreement between the insoles and the reference walkway system during both self-selected pace and hurried pace walking tasks. Although agreement is not as strong when estimating spatial metrics, the limits of agreement (LOA) still indicate that clinically important differences can be seen. Additional investigation into the insole derived center of pressure metrics from the insole sensor systems may be necessary as well as investigation into improved algorithms for capturing gait metrics during more real-world conditions in both types of systems. © 2024 IEEE.

关键词： Gait analysis

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Predicting Early Dropouts of an Active and Healthy Ageing App

arXiv

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arXiv 2023年

作者： Perifanis, Vasileios Michailidi, Ioanna Stamatelatos, Giorgos Drosatos, George Efraimidis, Pavlos S. Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67100 Greece

In this work, we present a machine learning approach for predicting early dropouts of an active and healthy ageing app. The presented algorithms have been submitted to the IFMBE Scientific Challenge 2022 (***), part of IUPESM WC 2022. We have processed the given database and generated seven datasets. We used pre-processing techniques to construct classification models that predict the adherence of users using dynamic and static features. We submitted 11 official runs and our results show that machine learning algorithms can provide high-quality adherence predictions. Based on the results, the dynamic features positively influence a model’s classification performance. Due to the imbalanced nature of the dataset, we employed oversampling methods such as SMOTE and ADASYN to improve the classification performance. The oversampling approaches led to a remarkable improvement of 10%. Our methods won first place in the IFMBE Scientific Challenge 2022. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Transfer learning for automated responses to the BDI questionnaire

Transfer learning for automated responses to the BDI questio...

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2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021

作者： Spartalis, Christoforos Drosatos, George Arampatzis, Avi Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67100 Greece

This paper describes the participation of the DUTH-ATHENA team of Democritus University of Thrace and Athena Research center in the eRisk 2021 task, which focuses on measuring the level of depression based on Reddit users' posts. We address this task using both feature-based and fine-tuning strategies for applying BERT-based representations. In the feature-based approaches, we examine the possibilities of a SBERT model based on RoBERTa, pre-trained on Natural language Inference (NLI) data and fine-tuned on STSb dataset to leverage transfer learning to depression-level estimation, and we achieve promising results. One of our runs ranks first in Average Hit Rate (AHR), while the others rank among the best four in the other evaluation metrics. Also, for the fine-tuning approach, we propose two predictive models that are built upon RoBERTa, which provide directions for future optimizations. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Social networking (online)

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SL-REDU GSL: A Large Greek Sign language Recognition Corpus

SL-REDU GSL: A Large Greek Sign Language Recognition Corpus

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Acoustics, speech, and Signal processing Workshops (ICASSPW), IEEE International Conference on

作者： Katerina Papadimitriou Galini Sapountzaki Kyriaki Vasilaki Eleni Efthimiou Stavroula-Evita Fotinea Gerasimos Potamianos Department of Electrical & Computer Engineering University of Thessaly Volos Greece Department of Special Education University of Thessaly Volos Greece Institute for Language & Speech Processing Athena Research & Innovation Center Athens Greece

We present a large multi-signer video corpus for the Greek Sign language (GSL), suitable for the development and evaluation of GSL recognition algorithms. The database has been collected as part of the “SL-ReDu” project that focuses on the education use-case of systematic teaching of GSL as a second language (L2). The project aims to assist this process by allowing self-monitoring and objective assessment of GSL learners’ productions through the use of recognition technology, thus requiring suitable data resources relevant to the aforementioned use-case. To this end, we present the SL-ReDu GSL corpus, an extensive RGB+D video collection of 21 informants with a duration of 36 hours, recorded under studio conditions, consisting of: (i) isolated signs; (ii) continuous signing (annotated at the sentence level); and (iii) fingerspelling of words. We provide a detailed description of the design and acquisition methods used to develop it, along with corpus statistics and a comparison to existing sign language datasets. The SL-ReDu GSL corpus, as well as proposed frameworks for recognition experiments on it, are publicly available at https://***/corpus.

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HFabD+M: A Web-based Platform for Automated Hyperledger Fabric Deployment and Management

HFabD+M: A Web-based Platform for Automated Hyperledger Fabr...

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Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Ioannis Zikos Andreas Sendros George Drosatos Pavlos S. Efraimidis Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Institute for Language and Speech Processing Athena Research Center Xanthi Greece

关键词： Distributed ledger Smart contracts Prototypes Systems architecture Companies Fabrics Blockchains

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