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SeqAug: Sequential Feature Resampling as a modality agnostic augmentation method

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arXiv 2023年

作者： Georgiou, Efthymios Potamianos, Alexandros School of Ece National Technical University of Athens Athens Greece Institute for Language and Speech Processing Athena Research Center Athens Greece

Data augmentation is a prevalent technique for improving performance in various machine learning applications. We propose SeqAug, a modality-agnostic augmentation method that is tailored towards sequences of extracted features. The core idea of SeqAug is to augment the sequence by resampling from the underlying feature distribution. Resampling is performed by randomly selecting feature dimensions and permuting them along the temporal axis. Experiments on CMU-MOSEI verify that SeqAug is modality agnostic;it can be successfully applied to a single modality or multiple modalities. We further verify its compatibility with both recurrent and transformer architectures, and also demonstrate comparable to state-of-the-art results. © 2023, CC BY.

关键词： Sentiment analysis

DIRECTED METRIC STRUCTURES ARISING IN LARGE language MODELS

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arXiv 2024年

作者： Gaubert, Stéphane Vlassopoulos, Yiannis INRIA and CMAP École polytechnique IP Paris CNRS France ATHENA Research Center Institute for Language and Speech Processing Athens Greece IHES Bures-sur-Yvette France

Large language Models are transformer neural networks which are trained to produce a probability distribution on the possible next words to given texts in a corpus, in such a way that the most likely word predicted is the actual word in the training text. In this paper we find what is the mathematical structure defined by such conditional probability distributions of text extensions. Changing the view point from probabilities to -log probabilities we observe that the subtext order is completely encoded in a metric structure defined on the space of texts L, by -log probabilities. We then construct a metric polyhedron P (L) and an isometric embedding (called Yoneda embedding) of L into P (L) such that texts map to generators of certain special extremal rays. We explain that P (L) is a (min, +) (tropical) linear span of these extremal ray generators. The generators also satisfy a system of (min +) linear equations. We then show that P (L) is compatible with adding more text and from this we derive an approximation of a text vector as a Boltzmann weighted linear combination of the vectors for words in that text. We then prove a duality theorem showing that texts extensions and text restrictions give isometric polyhedra (even though they look a priory very different). Moreover we prove that P (L) is the lattice closure of (a version of) the so called, Isbell completion of L which turns out to be the (max, +) span of the text extremal ray generators. All constructions have interpretations in category theory but we don’t use category theory explicitly. The categorical interpretations are briefly explained in an appendix. In the final appendix we describe how the syntax to semantics problem could fit in a general well known mathematical *** Codes 14T90, 18D20, 52B12, 51F99, 68T50 Copyright © 2024, The Authors. All rights reserved.

关键词： Probability distributions

User-centered Implementation of Rehabilitation Exercising on an Assistive Robotic Platform 22nd

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User-centered Implementation of Rehabilitation Exercising on...

22nd International Conference on Human Computer Interaction, HCII 2020

作者： Papageorgiou, Xanthi S. Tsampounaris, George Karavasili, Alexandra Efthimiou, Eleni Fotinea, Stavroula-Evita Vacalopoulou, Anna Karioris, Panagiotis Koureta, Fotini Alexopoulou, Despina Dimou, Dimitris Institute for Language and Speech Processing/ATHENA RC Athens Greece DIAPLASIS Rehabilitation Center Kalamata Greece

ISBN: (纸本)9783030601485

The paper focuses on the method and steps implementing a suite of rehabilitation exercises on an assistive robotic platform. The suite is based on extensive user needs identification procedures and consultation with medical and rehabilitation experts. For the design of the human-robot interaction (HRI) component of the platform, the user centered approach was adopted, which in this case employed a range of multimodal interaction facilities including a free user-robot dialogue, visual and speech signals. © 2020, Springer Nature Switzerland AG.

关键词： Human robot interaction

Privacy-Preserving Blockchain-Based Solutions in the Internet of Things 1

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6th EAI International Conference on Science and Technologies for Smart Cities, SmartCity 2020

作者： Zapoglou, Nikolaos Patsakos, Ioannis Drosatos, George Rantos, Konstantinos Department of Computer Science International Hellenic University Kavala Greece Institute for Language and Speech Processing Athena Research Center Xanthi Greece

ISBN: (数字)9783030760632

ISBN: (纸本)9783030760625

Internet of Things (IoT) is a promising, relatively new technology that develops "smart" networks with a variety of uses and applications (e.g., smart cities, smart home and autonomous cars). The diversity of protocols, technologies and devices that IoT consists of, even though they add in value and utility, they create major privacy issues that can be exploited by malicious entities to benefit from or even violate privacy of IoT users. The special features of blockchain technology, such as immutability, transparency, accessibility, autonomy and decentralisation, has led the academics and the industry to search for further uses of it, besides financial applications (e.g., Bitcoin) that was initially applied. This paper is a survey on the existing literature regarding blockchain-based privacy-preserving solutions that have been proposed specifically for the IoT to address personal data protection and preserve user privacy. © 2021, ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

Three Gods in Eleusis: Discovering Tangible and Intangible Cultural Heritage of Ancient Eleusis 2021

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Three Gods in Eleusis: Discovering Tangible and Intangible C...

25th Pan-Hellenic Conference on Informatics

作者： Helena G. Theodoropoulou Chairi Kiourt Natasa Michailidou George A. Ioannakis Paraskevi Motsiou Yiannis Mourthos Anestis Koutsoudis George Pavlidis Despoina Tsiafaki Institute for Language and Speech Processing Athena-Research & Innovation Center in Information Communication and Knowledge Technologies Greece

ISBN: (纸本)9781450395557

The present study concerns the design and implementation of a serious game for the on-site and remote promotion and dissemination of knowledge regarding the mythology of ancient Eleusis in an interactive, fun and educational way. Specifically, while playing, the user discovers artifacts exhibited in the archaeological museum and related to the archaeological site of Eleusis, unfolding the content of the myth of Persephone. The story takes place in a realistic three-dimensional virtual environment that simulates the Archaeological Museum of Eleusis. The selected archaeological artifacts, in combination with the narration of the myth of Persephone, transfer the knowledge about the historicity of the place in an experientially playful way. The main purpose of the study is to introduce a novel, interactive playful narrative digital museum experience, in which the user is guided through a mechanism to simultaneously acquire knowledge both for the intangible and tangible heritage of a site with significant historical value. The evolution of the scenario (story) is dynamic and based on the choices of the user. The content (text etc.) of the system is presented through a dynamic multilingual approach, which offers the management of multiple different languages and personalized content based on the target group. The system relies on cross-platform technologies to cover a wide range of users.

关键词： Game-Based Learning Eleusis Serious Games Myth of Persephone Tangible and Intangible Cultural Heritage

Predicting Early Dropouts of an Active and Healthy Ageing App

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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

Transfer learning for automated responses to the BDI questionnaire

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

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)

ADAPTED MULTIMODAL BERT WITH LAYER-WISE FUSION FOR SENTIMENT ANALYSIS

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arXiv 2022年

作者： Chlapanis, Odysseas S. Paraskevopoulos, Georgios Potamianos, Alexandros National Technical University of Athens Athens Greece Institute for Language and Speech Processing Athena Research Center Athens Greece

Multimodal learning pipelines have benefited from the success of pretrained language models. However, this comes at the cost of increased model parameters. In this work, we propose Adapted Multimodal BERT (AMB), a BERT-based architecture for multimodal tasks that uses a combination of adapter modules and intermediate fusion layers. The adapter adjusts the pretrained language model for the task at hand, while the fusion layers perform task-specific, layer-wise fusion of audio-visual information with textual BERT representations. During the adaptation process the pre-trained language model parameters remain frozen, allowing for fast, parameter-efficient training. In our ablations we see that this approach leads to efficient models, that can outperform their fine-tuned counterparts and are robust to input noise. Our experiments on sentiment analysis with CMU-MOSEI show that AMB outperforms the current state-of-the-art across metrics, with 3.4% relative reduction in the resulting error and 2.1% relative improvement in 7−class classification accuracy. © 2022, CC BY-NC-SA.

关键词： Sentiment analysis

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

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

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