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arXiv 2024年

作者： Liu, Bei Qian, Yanmin The Auditory Cognition and Computational Acoustics Lab. The Department of Computer Science and Engineering The MoE Key Laboratory of Artifificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China

Recent speaker verification (SV) systems have shown a trend toward adopting deeper speaker embedding extractors. Although deeper and larger neural networks can significantly improve performance, their substantial memory requirements hinder training on consumer GPUs. In this paper, we explore a memory-efficient training strategy for deep speaker embedding learning in resource-constrained scenarios. Firstly, we conduct a systematic analysis of GPU memory allocation during SV system training. Empirical observations show that activations and optimizer states are the main sources of memory consumption. For activations, we design two types of reversible neural networks which eliminate the need to store intermediate activations during back-propagation, thereby significantly reducing memory usage without performance loss. For optimizer states, we introduce a dynamic quantization approach that replaces the original 32-bit floating-point values with a dynamic tree-based 8-bit data type. Experimental results on VoxCeleb demonstrate that the reversible variants of ResNets and DF-ResNets can perform training without the need to cache activations in GPU memory. In addition, the 8-bit versions of SGD and Adam save 75% of memory costs while maintaining performance compared to their 32-bit counterparts. Finally, a detailed comparison of memory usage and performance indicates that our proposed models achieve up to 16.2x memory savings, with nearly identical parameters and performance compared to the vanilla systems. In contrast to the previous need for multiple high-end GPUs such as the A100, we can effectively train deep speaker embedding extractors with just one or two consumer-level 2080Ti GPUs. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphics processing unit

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Robots in education: Influence of Regulatory Focus Theory

Robots in education: Influence of Regulatory Focus Theory

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Xiaoxuan Hei Heng Zhang Adriana Tapus Computer Science and System Engineering Department (U2IS) Autonomous Systems and Robotics Lab ENSTA Paris Institut Polytechnique de Paris Paris France

The Covid-19 pandemic has massively developed the use of distance learning. The limits of this practice have gradually come to light, both for students and for teachers. It is now crucial to design alternative solutions to overcome the shortcomings of videoconferencing in terms of involvement, concentration, learning, and equity. Social robots are increasingly used as tutors in the educational context and help improve teaching efficiency. Many psychology-based principles have been applied in education to guide instructional strategies, motivate students, and create a positive and productive learning environment. In this work, we use Regulatory Focus Theory (RFT), which categorizes an individual’s motivation into two types: Promotion and Prevention. Promotion-focused individuals are motivated by the potential for growth and achievement, whereas prevention-focused individuals are motivated by the potential for avoiding negative outcomes. Based on RFT, we aim to explore if and how the regulatory-focused behavior of the tutor robot can affect participants’ learning outcomes. In this work, a language learning scenario was designed with two conditions: (1) a robot tutor with promotion-focused behavior, (2) a robot tutor with prevention-focused behavior. The results are encouraging and support that promotion robot tutor can increase the learning efficiency of promotion participants and prevention robot tutor will enhance the learning interest of prevention participants.

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Towards the Influence of Human Observer Eye-Movements on Discriminating Between Perceived Sociability of Different Robots 54

Towards the Influence of Human Observer Eye-Movements on Dis...

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54th International Symposium on robotics, ISR Europe 2022

作者： Kühnlenz, Kolja Kühnlenz, Barbara Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and Arts CoburgD-96450 Germany Academic Center for Sciences and Humanities Coburg University of Applied Sciences and Arts CoburgD-96450 Germany

ISBN: (纸本)9783800758920

Results from a study on Perceived Sociability of a robot dependent on different gaze behaviors of human observers are presented. A 2x2 between-subjects design is used with independent variables’gaze-behavior’ (static/dynamic) and’robot’ (appearance) of a displayed robot head. Participants are told to fixate a point on a screen, which is either static or moving linearly, while a picture of one of two robot heads is shown in the covert visual attention region of the human observer. The robots differ in the level of human-likeness (mechanistic vs. anthropomorphic). Significant results show, that Perceived Sociability depends on the interaction of gaze-behavior and robot appearance. The findings show, that eye-movements influence covert perception of robot attributes and contribute to a better understanding of gaze dependent perception of robot appearance with potential practical implications of situational perception of anthropomorphic attributes like sociability situationally impacting interaction with the robot. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Eye movements

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A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data

A Trustable LSTM-Autoencoder Network for Cyberbullying Detec...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Akter, Mst Shapna Shahriar, Hossain Cuzzocrea, Alfredo Wu, Fan Rodriguez-Cardenas, Juanjose University of West Florida Department of Intelligent Systems and Robotics United States University of West Florida Center of Cybersecurity United States University of Calabria IDEA Lab Rende Italy Tuskegee University Dept. of Computer Science Tuskegee United States Kennesaw State University Dept. of Information Technology Georgia United States

ISBN: (纸本)9798350324457

Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long ShortTerm Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models' performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper. © 2023 IEEE.

关键词： Cyber-bullying Deep Learning Natural Language Processing Neural Networks

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Self-Supervised Learning Using Noisy-Latent Augmentation

Self-Supervised Learning Using Noisy-Latent Augmentation

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IEEE International Conference on Systems, Man and Cybernetics

作者： Rasa Khosrowshahli Shahryar Rahnamayan Azam Asilian Bidgoli Masoud Makrehchi Department of Electrical Computer and Software Engineering Nature-Inspired Computational Intelligence (NICI) Lab Ontario Tech University Oshawa ON Canada Engineering Department Nature-Inspired Computational Intelligence (NICI) Lab Brock University St. Catharines ON Canada Nature-Inspired Computational Intelligence (NICI) Lab Faculty of Science Wilfrid Laurier University Waterloo ON Canada Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada

Generally speaking, labeled data is difficult and expensive to provide for applications in machine learning and data mining. One of the earliest approaches to tackle this problem is semi-supervised self-training to take advantages of labeled and unlabeled data to create pseudo-labeled data. However, reaching a high level of confidence to predict the unseen data by a classifier with a limited number of labeled samples is a challenging task. Generating pseudo-labeled data can collaboratively improve the self-training to increase its confidence for further predictions. This paper proposes a collaborative framework between augmentation and self-training to accurately train a model with very limited labeled data. Our framework includes two components where the first component is an unsupervised technique to augment labeled data and feed to the second component which is self-training. The first component uses a custom variational autoencoder architecture (VAE) to generate new samples by adding randomly generated noise to encoded latent representation. As a result, the augmentation component can generate the unique and unexplored images with respect to limited input data distribution. We evaluated the proposed framework on handwritten MNIST image dataset. The conducted experiment shows that the generative component can be helpful in overcoming the problem of inaccurate self-training prediction when sufficient labeled data is not accessible.

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Recurrent Lassa Fever Outbreaks: Spatiotemporal Analysis and Modelling of Environmental Intervention Strategies

Recurrent Lassa Fever Outbreaks: Spatiotemporal Analysis and...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Sampson Akwafuo Ali Hussain Christopher Ihinegbu Department of Computer Science Disaster and Data Intelligence (CEDDI) lab Computational Epidemiology California State University Fullerton CA USA Department of Geography University of Bonn Bonn Germany

Lassa Fever (LF), caused by Mastomys natalensis, a rodent specie, is an acute zoonotic viral hemorrhagic illness. It is endemic in most countries of West Africa, and may spread to other parts of the world, if not checked, according to the Center for Diseases Control and Prevention. In this paper, we present a spatiotemporal analysis of previous outbreaks of Lassa Fever in Nigeria, and hybrid mathematical and agent-based models to understand the dynamics of the transmission and environmental factors affecting the outbreaks. Using existing data on recorded cases and available intervention details, we model the effectiveness of these control measures on curtailing the outbreaks. According to our findings, efforts aimed at isolating newly infected individuals, coupled with treatment, appear to yield the highest efficiency amongst all studied control measures. This is closely followed by efforts aimed at controlling infected and susceptive rodent populations. In low-resource countries, it is recommended that the few available resources be channeled in this order of priority in the management of Lassa Fever cases: isolation, treatment and rodent control.

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Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFD)to geoscience and climate ***,much effort has been given in combining DA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

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Deep learning for brain age estimation: A systematic review

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Information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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Stochastic Sensitivity Regularized Autoencoder for Robust Feature Learning 21

Stochastic Sensitivity Regularized Autoencoder for Robust Fe...

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21st IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022

作者： Zhang, Jianjun Wang, Ting Ng, Wing W. Y. Pedrycz, Witold Kwong, Sam School of Computer Science and Engineering South China University of Technology Guangzhou Guangdong China School of Medicine South China University of Technology Guangzhou First People's Hospital Department of Radiology Guangdong Guangzhou China School of Computer Science and Engineering South China University of Technology Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Guangzhou Guangdong China University of Alberta Department of Electrical and Computer Engineering EdmontonAB Canada Hong Kong City University Department of Computer Science Hong Kong Hong Kong

ISBN: (纸本)9781665490849

We present a new regularized autoencoder for robust feature learning. The regularization, implying stochastic sensitivity, is defined as the sum of entries of the absolute covariance matrix of the output perturbation at each layer of the autoencoder. The advantages of the stochastic sensitivity regularization are two-fold. Firstly, we show that the classical Frobenius norm regularization effectively enforces the network to be insensitive to input perturbation and that the Frobenius norm regularization is a special case of the proposed stochastic sensitivity regularization which enables the proposed method to train an autoencoder for robust feature learning. Secondly, we also show that the stochastic sensitivity regularization attempts to drive the network to learn a set of decorrelated feature maps which removes redundant information and thus improves generalization capabilities. These two properties enable the autoencoder to learn a set of robust and diverse feature maps. Finally, the efficacy and the robustness of the proposed regularization method are confirmed a nd quantified by comparing it against existing regularized auto encoders over a range of tasks. © 2022 IEEE.

关键词： Covariance matrix

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Online Indoor Localization Using DOA of Wireless Signals

arXiv

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arXiv 2022年

作者： Latif, Ehsan Parasuraman, Ramviyas The Heterogeneous Robotics Lab. Department of Computer Science University of Georgia AthensGA30602 United States

Localization of a wireless mobile device or a robot in indoor and GPS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional cameras and LIDAR-based alternative sensing and localization modalities may fail. We propose a method for estimating the location of a mobile robot in relation to static wireless sensor nodes (WSN) deployed in the environment. The method employs a novel particle filter that updates its weights using a Gauss probability over Direction of Arrival (DOA) estimate in conjunction with the mobile robot’s mobility model. We evaluate and validate the proposed method in terms of accuracy and computational efficiency through extensive simulations and public real-world measurement datasets, comparing with standard state-of-the-art localization approaches. The results show considerably high meter-level localization accuracy balanced by the high computational efficiency, enabling it to use online without a need for a dedicated offline phase as in typical fingerprint-based localization algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Indoor positioning systems

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