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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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End-to-end Gesture Recognition Framework for the Identification of Allergic Rhinitis Symptoms

End-to-end Gesture Recognition Framework for the Identificat...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Pantelis Tzamalis Andreas Bardoutsos Dimitris Markantonatos Christoforos Raptopoulos Sotiris Nikoletseas Xenophon Aggelides Nikos Papadopoulos Computer Technology Institute and Press "Diophantus" (CTI) Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece Allergy Dpt 2nd Pediatric Clinic University of Athens Athens Greece Division of Infection Immunity and Respiratory Medicine University of Manchester Manchester UK

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

Human Gesture Recognition (HGR) using smart wearable IoT devices has emerged as a new field in human-centered computing regarding various domains. Though there are many research works related to data processing methodologies and Neural Networks architectures in this field, a lack of research on how to efficiently identify and interpret the AI models’ exports into human gestures is observed. This paper proposes an innovative end-to-end approach of how to solve and evaluate effectively a major part of HGR problems in a real-world scenario, in real-time. This is achieved with the effective utilization of data processing methods, the adoption, and extension of a cutting-edge Deep Learning model architecture, as well as the introduction and implementation in practice of innovative methods, both for interpretation and evaluation, that increase the trustworthiness of the model’s *** a case study, we deployed the introduced pipeline into a real-world scenario of gestures’ identification and classification regarding allergic symptoms. We adopted multidisciplinarity by collaborating with recognized allergists that validated the whole approach in real patients via two pilot phases. As a result, by delivering a real-world application of our approach, we achieved a superior performance concerning the reliability of the pipeline, being 91.6% in our laboratory pilot phase and 81.4% in patients’ pilot data. Lastly, it is worth mentioning here that our framework can be employed in most HGR problems with minor modifications in data processing and learning procedure configuration.

关键词： Codes Wearable computers Pipelines Neural networks Gesture recognition computer architecture Predictive models

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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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Towards countering hate speech against journalists on social media

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Online Social Networks and Media 2020年 17卷

作者： Charitidis, Polychronis Doropoulos, Stavros Vologiannidis, Stavros Papastergiou, Ioannis Karakeva, Sophia DataScouting 30 Vakchou Street Thessaloniki54629 Greece Dpt of Computer Informatics and Telecommunications Engineering International Hellenic University Terma Magnisias Serres62124 Greece

The damaging effects of hate speech on social media are evident during the last few years, and several organizations, researchers and social media platforms tried to harness them in various ways. Despite these efforts, social media users are still affected by hate speech. The problem is even more apparent to social groups that promote public discourse, such as journalists. In this work, we focus on countering hate speech that is targeted to journalistic social media accounts. To accomplish this, a group of journalists assembled a definition of hate speech, taking into account the journalistic point of view and the types of hate speech that are usually targeted against journalists. We then compile a large pool of tweets referring to journalism-related accounts in multiple languages. In order to annotate the pool of unlabeled tweets according to the definition, we follow a concise annotation strategy that involves active learning annotation stages. The outcome of this paper is a novel, publicly available collection of Twitter datasets in five different languages. Additionally, we experiment with state-of-the-art deep learning architectures for hate speech detection and use our annotated datasets to train and evaluate them. Finally, we propose an ensemble detection model that outperforms all individual models. © 2020 Elsevier B.V.

关键词： Supervised learning

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Enriching social analytics with latent Twitter image information

Enriching social analytics with latent Twitter image informa...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Gerasimos Razis Georgios Theofilou Ioannis Anagnostopoulos Computer Science and Biomedical Informatics Dpt. University of Thessaly Lamia Greece School of Electrical & Computer Engineering National Technical University of Athens Athens Greece

ISBN: (数字)9781728159195

ISBN: (纸本)9781728159201

In this paper, we propose a framework that uses latent information from Twitter images by employing the Google Cloud Vision API platform aiming at enriching social analytics with semantics and textual information. Our study reveals that user-generated content, linked data as well as hidden concepts and textual information from social images can be highly considered for enriching social analytics. Finally, we publish our annotated dataset for further use and evaluation from our research community.

关键词： Social networking (online) Linked data Conferences Semantics Blogs User-generated content

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Design and implementation of an open source Greek pos tagger and entity recognizer using spaCy

arXiv

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arXiv 2019年

作者： Partalidou, Eleni Spyromitros-Xioufis, Eleftherios Doropoulos, Stavros Vologiannidis, Stavros Diamantaras, Konstantinos I. DataScouting 30 Vakchou Street Thessaloniki54629 Greece Dpt of Computer Informatics and Telecommunications Engineering International Hellenic University Terma Magnisias Serres62124 Greece Dpt of Informatics and Electronic Engineering International Hellenic University Sindos Thessaloniki57400 Greece

This paper proposes a machine learning approach to part-of-speech tagging and named entity recognition for Greek, focusing on the extraction of morphological features and classification of tokens into a small set of classes for named entities. The architecture model that was used is introduced. The greek version of the spaCy platform was added into the source code, a feature that did not exist before our contribution, and was used for building the models. Additionally, a part of speech tagger was trained that can detect the morphology of the tokens and performs higher than the state-of-the-art results when classifying only the part of speech. For named entity recognition using spaCy, a model that extends the standard ENAMEX type (organization, location, person) was built. Certain experiments that were conducted indicate the need for flexibility in out-of-vocabulary words and there is an effort for resolving this issue. Finally, the evaluation results are discussed. Copyright © 2019, The Authors. All rights reserved.

关键词： Computational linguistics

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Design and implementation of an open source Greek POS Tagger and Entity Recognizer using spaCy

Design and implementation of an open source Greek POS Tagger...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Eleni Partalidou Eleftherios Spyromitros-Xioufis Stavros Doropoulos Stavros Vologiannidis Konstantinos I. Diamantaras DataScouting Thessaloniki Greece Dpt of Computer Informatics and Telecommunications Engineering International Hellenic University Serres Greece International Hellenic University Thessaloniki Greece

ISBN: (纸本)9781728150376;9781450369343

关键词： Tagging Task analysis Training Feature extraction Natural language processing Encyclopedias

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IxaMed at CLEF eHealth 2018 task 1: ICD10 coding with a sequence-to-sequence approach 19

IxaMed at CLEF eHealth 2018 task 1: ICD10 coding with a sequ...

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19th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2018

作者： Atutxa, A. Casillas, A. Ezeiza, N. Fresno, V. Goenaga, I. Gojenola, K. Martínez, R. Oronoz, M. Perez-De-Viñaspre, O. Dpt. Languages and Computer Systems School of Engineering UPV EHU Bilbao Spain Dpt. Electricity and Electronics Fac. of Science and Technology UPV EHU Leioa Spain Dpt. Languages and Computer Systems Faculty of Informatics UPV EHU Donostia-San Sebastian United States Dpt. Languages and Computer Systems School of Computer Engineering UNED Madrid Spain

Hospital systems routinely assign disease codes (ICD10 codes) to medical records. The challenge stands on treating natural and nonstandard language in which doctors express their diagnoses and, additionally, to solve a large-scale classification problem, as there are thousands of possible codes. In this working notes paper, we present our system and the results of the CLEF 2018 eHealth Evaluation Task 1 on Multilingual Information Extraction-ICD10 coding. This benchmark addresses information extraction in written text with focus on several languages, specifically Hungarian, Italian and French. The goal is to automatically assign ICD10 codes to diagnostic terms of death certificates. The problem can be cast in different ways, for example as a multilabel classification task or as sequence-to-sequence prediction. Our proposal follows this last approach, with promising results, well above the average results for the task. It only relies on the material provided by the task organizers, allowing the application of the same system to all datasets.

关键词： Machine learning

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Secure and Efficient RNS Software Implementation for Elliptic Curve Cryptography

Secure and Efficient RNS Software Implementation for Ellipti...

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IEEE European Symposium on Security and Privacy Workshops

作者： Apostolos P. Fournaris Louiza Papachristodoulou Nicolas Sklavos Electrical and Computer Engineering Dpt. University of Patras Greece Digital Security Group Radboud University Nijmegen The Netherlands Computer Engineering & Informatics Dpt. University of Patras Greece

ISBN: (纸本)9781538622452

Elliptic Curve Cryptography operations rely heavily on the strong security of scalar multiplication. However, this operation is vulnerable to side channel (SCA) and fault injection (FA) attacks. The use of alternative arithmetic systems like Residue Number System (RNS) for all scalar multiplication underline operations has been proposed as an efficient countermeasure approach for the above attacks. In RNS, a number is represented as a set of smaller numbers, where each one is the result of the modular reduction with a given moduli basis. Under certain requirements, a number can be uniquely transformed from the integers to the RNS domain (and vice versa) and all arithmetic operations can be performed in RNS. This representation provides an inherent SCA and FA resistance to many attacks and can be further enhanced by additional RNS arithmetic manipulations or more traditional algorithmic countermeasures. In this paper, extending our previous work, we explore the potentials of RNS as an SCA and FA countermeasure. A description of RNS based SCA and FA resistance means is provided through appropriate scalar multiplication algorithmic variations, traces of the proposed algorithm are collected and the results are analyzed regarding the RNS countermeasure strength. More specifically, in this paper, a secure RNS based Montgomery Power Ladder based scalar multiplication algorithm is provided and is implemented on an ARM Cortex A7 processor. The implementation SCA-FA resistance is evaluated by collecting preliminary leakage trace results that validate our initial assumptions.

关键词： Resistance Manganese Elliptic curve cryptography Algorithm design and analysis Europe Privacy

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