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computer-Mediated Communication in Adults with and without Moderate-to-Severe Traumatic Brain Injury: Survey of Social Media Use

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JMIR Rehabilitation and Assistive Technologies 2021年第3期8卷 e26586页

作者： Morrow, Emily L. Zhao, Fangyun Turkstra, Lyn Toma, Catalina Mutlu, Bilge Duff, Melissa C. Department of Hearing & Speech Sciences Vanderbilt University Medical Center Nashville TN United States Department of Psychology University of Wisconsin-Madison Madison WI United States Department of Computer Sciences University of Wisconsin-Madison Madison WI United States Speech-Language Pathology Program School of Rehabilitation Science McMaster University Hamilton ON Canada Department of Communication Arts University of Wisconsin-Madison Madison WI United States

Background: Individuals with a history of traumatic brain injury (TBI) report fewer social contacts, less social participation, and more social isolation than noninjured peers. Cognitive-communication disabilities may prevent individuals with TBI from accessing the opportunities for social connection afforded by computer-mediated communication, as individuals with TBI report lower overall usage of social media than noninjured peers and substantial challenges with accessibility and usability. Although adaptations for individuals with motor and sensory impairments exist to support social media use, there have been no parallel advances to support individuals with cognitive disabilities, such as those exhibited by some people with TBI. In this study, we take a preliminary step in the development process by learning more about patterns of social media use in individuals with TBI as well as their input and priorities for developing social media adaptations. Objective: This study aims to characterize how and why adults with TBI use social media and computer-mediated communication platforms, to evaluate changes in computer-mediated communication after brain injury, and to elicit suggestions from individuals with TBI to improve access to social media after injury. Methods: We conducted a web-based survey of 53 individuals with a chronic history of moderate-to-severe TBI and a demographically matched group of 51 noninjured comparison peers. Results: More than 90% of participants in both groups had an account on at least one computer-mediated communication platform, with Facebook and Facebook Messenger being the most popular platforms in both groups. Participants with and without a history of TBI reported that they use Facebook more passively than actively and reported that they most frequently maintain web-based relationships with close friends and family members. However, participants with TBI reported less frequently than noninjured comparison participants that they use syn

关键词： Cognitive communication Disability Rehabilitation Social media Traumatic brain injury

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Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis

arXiv

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arXiv 2020年

作者： Raj, Desh Denisov, Pavel Chen, Zhuo Erdogan, Hakan Huang, Zili He, Maokui Watanabe, Shinji Du, Jun Yoshioka, Takuya Luo, Yi Kanda, Naoyuki Li, Jinyu Wisdom, Scott Hershey, John R. Center for Language and Speech Processing Johns Hopkins University BaltimoreMD United States Institute for Natural Language Processing University of Stuttgart Germany Microsoft Corp RedmondWA United States Google Research CambridgeMA United States University of Science and Technology of China HeFei China Department of Electrical Engineering Columbia University NY United States

Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation. With technical advances in systems dealing with speech separation, speaker diarization, and automatic speech recognition (ASR) in the last decade, it has become possible to build pipelines that achieve reasonable error rates on this task. In this paper, we propose an end-to-end modular system for the LibriCSS meeting data, which combines independently trained separation, diarization, and recognition components, in that order. We study the effect of different state-of-the-art methods at each stage of the pipeline, and report results using task-specific metrics like SDR and DER, as well as downstream WER. Experiments indicate that the problem of overlapping speech for diarization and ASR can be effectively mitigated with the presence of a well-trained separation module. Our best system achieves a speaker-attributed WER of 12.7%, which is close to that of a non-overlapping ASR. © 2020, CC-BY.

关键词： speech recognition

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Domain adversarial fine-tuning as an effective regularizer

arXiv

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arXiv 2020年

作者： Vernikos, Giorgos Margatina, Katerina Chronopoulou, Alexandra Androutsopoulos, Ion School of ECE National Technical University of Athens Greece Computer Science Department University of Sheffield United Kingdom Center for Information and Language Processing LMU Munich Germany Department of Informatics Athens University of Economics and Business Greece

In Natural language processing (NLP), pretrained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results. However, standard fine-tuning can degrade the general-domain representations captured during pretraining. To address this issue, we introduce a new regularization technique, AFTER;domain Adversarial Fine-Tuning as an Effective Regularizer. Specifically, we complement the task-specific loss used during fine-tuning with an adversarial objective. This additional loss term is related to an adversarial classifier, that aims to discriminate between in-domain and out-of-domain text representations. In-domain refers to the labeled dataset of the task at hand while out-of-domain refers to unlabeled data from a different domain. Intuitively, the adversarial classifier acts as a regularizer which prevents the model from overfitting to the task-specific domain. Empirical results on various natural language understanding tasks show that AFTER leads to improved performance compared to standard fine-tuning. Copyright © 2020, The Authors. All rights reserved.

关键词： Natural language processing systems

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Correction to: Attention based video captioning framework for Hindi

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Multimedia Systems 2021年第1期29卷 453-453页

作者： Singh, Alok Singh, Thoudam Doren Bandyopadhyay, Sivaji Center for Natural Language Processing (CNLP) NIT Silchar India Department of Computer Science and Engineering National Institute of Technology Silchar India

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Cross-cluster asymmetric group key agreement for wireless sensor networks

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science China(Information sciences) 2018年第4期61卷 226-228页

作者： Jiamin ZHENG Yu'an TAN Qikun ZHANG Xiaosong ZHANG Liehuang ZHU Quanxin ZHANG School of Computer Science and Technology Beijing Institute of Technology Research Center of Massive Language Information Processing and Cloud Computing Application Institute of Computer and Communication Engineering Zhengzhou University of Light Industry Department of Computer Science and Technology Tangshan University

Wireless sensor networks have some obvious negative characteristics,such as limited communication range,energy constraints,and vulnerability.A group key agreement in this environment requires lightweight cross-cluster... 详细信息

关键词： Cross-cluster asymmetric group key agreement for wireless sensor networks

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speech Enhancement via Deep Spectrum Image Translation Network 26

Speech Enhancement via Deep Spectrum Image Translation Netwo...

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26th National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019

作者： Kashani, Hamidreza Baradaran Jodeiri, Ata Goodarzi, Mohammad Mohsen Rezaei, Iman Sarraf Amirkabir University of Technology Electrical Engineering Faculty Tehran Iran College of Engineering University of Tehran School of Electrical Computer Engineering Tehran Iran Buein Zahra Technical University Department of Electrical and Computer Engineering Qazvin Iran Speech and Language Processing Group Research Center for Development of Advanced Technologies Tehran Iran

ISBN: (纸本)9781728156637

Quality and intelligibility of speech signals are degraded under additive background noise which is a critical problem for hearing aid and cochlear implant users. Motivated to address this problem, we propose a novel speech enhancement approach using a deep spectrum image translation network. To this end, we suggest a new architecture, called VGG19-UNet, where a deep fully convolutional network known as VGG19 is embedded at the encoder part of an image-to-image translation network, i.e. U-Net. Moreover, we propose a perceptuallymodified version of the spectrum image that is represented in Mel frequency and power-law non-linearity amplitude domains, representing good approximations of human auditory perception model. By conducting experiments on a real challenge in speech enhancement, i.e. unseen noise environments, we show that the proposed approach outperforms other enhancement methods in terms of both quality and intelligibility measures, represented by PESQ and ESTOI, respectively. © 2019 IEEE.

关键词： Audition

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MIMO-speech: End-to-end multi-channel multi-speaker speech recognition

arXiv

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arXiv 2019年

作者： Chang, Xuankai Zhang, Wangyou Qian, Yanmin Le Roux, Jonathan Watanabe, Shinji Center for Language and Speech Processing Johns Hopkins University United States SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University China United States

Recently, the end-to-end approach has proven its efficacy in monaural multi-speaker speech recognition. However, high word error rates (WERs) still prevent these systems from being used in practical applications. On the other hand, the spatial information in multi-channel signals has proven helpful in far-field speech recognition tasks. In this work, we propose a novel neural sequence-to-sequence (seq2seq) architecture, MIMO-speech, which extends the original seq2seq to deal with multi-channel input and multi-channel output so that it can fully model multi-channel multi-speaker speech separation and recognition. MIMO-speech is a fully neural end-to-end framework, which is optimized only via an ASR criterion. It is comprised of: 1) a monaural masking network, 2) a multi-source neural beamformer, and 3) a multi-output speech recognition model. With this processing, the input overlapped speech is directly mapped to text sequences. We further adopted a curriculum learning strategy, making the best use of the training set to improve the performance. The experiments on the spatialized wsj1-2mix corpus show that our model can achieve more than 60% WER reduction compared to the single-channel system with high quality enhanced signals (SI-SDR = 23.1 dB) obtained by the above separation function. Copyright © 2019, The Authors. All rights reserved.

关键词： Beamforming

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Neural polysynthetic language modelling

arXiv

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arXiv 2020年

作者： Schwartz, Lane Tyers, Francis Levin, Lori Kirov, Christo Littell, Patrick Lo, Chi-kiu Prud’hommeaux, Emily Park, Hyunji Hayley Steimel, Kenneth Knowles, Rebecca Micher, Jeffrey Strunk, Lonny Liu, Han Haley, Coleman Zhang, Katherine J. Jimerson, Robbie Andriyanets, Vasilisa Muis, Aldrian Obaja Otani, Naoki Park, Jong Hyuk Zhang, Zhisong Department of Linguistics University of Illinois at Urbana-Champaign Department of Linguistics Indiana University Language Technologies Institute Carnegie Mellon University Google National Research Council of Canada Department of Computer Science Boston College Center for Language and Speech Processing Johns Hopkins University National Research Council of Canada Army Research Laboratory Carnegie Mellon University Department of Linguistics University of Washington Department of Computer Science University of Colorado Boulder United States Johns Hopkins University Carnegie Mellon University Rochester Institute of Technology Moscow Higher School of Economics

Many techniques in modern computational linguistics and natural language processing (NLP) make the assumption that approaches that work well on English and other widely used European (and sometimes Asian) languages are "language agnostic" – that is that they will also work across the typologically diverse languages of the world. In high-resource languages, especially those that are analytic rather than synthetic, a common approach is to treat morphologically-distinct variants of a common root (such as dog and dogs) as completely independent word types. Doing so relies on two main assumptions: that there exist a limited number of morphological inflections for any given root, and that most or all of those variants will appear in a large enough corpus (conditioned on assumptions about domain, etc.) so that the model can adequately learn statistics about each variant. Approaches like stemming, lemmatization, morphological analysis, subword segmentation, or other normalization techniques are frequently used when either of those assumptions are likely to be violated, particularly in the case of synthetic languages like Czech and Russian that have more inflectional morphology than English. Within the NLP literature, agglutinative languages like Finnish and Turkish are commonly held up as extreme examples of morphological complexity that challenge common modelling assumptions. Yet, when considering all of the world’s languages, Finnish and Turkish are closer to the average case in terms of synthesis. When we consider polysynthetic languages (those at the extreme of morphological complexity), even approaches like stemming, lemmatization, or subword modelling may not suffice. These languages have very high numbers of hapax legomena (words appearing only once in a corpus), underscoring the need for appropriate morphological handling of words, without which there is no hope for a model to capture enough statistical information about those words. Moreover, many of these language

关键词： Natural language processing systems

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Trends in Silicates in the β Pictoris Disk

arXiv

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arXiv 2022年

作者： Lu, Cicero X. Chen, Christine H. Sargent, B.A. Watson, Dan M. Lisse, Carey M. Green, Joel D. Sitko, Michael L. Mittal, Tushar Lebouteiller, V. Sloan, G.C. Rebollido, Isabel Hines, Dean C. Girard, Julien H. Werner, Michael W. Stapelfeldt, Karl R. Wu, Winston Worthen, Kadin Department of Physics and Astronomy The Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States Space Telescope Science Institute 3700 San Martin Dr. BaltimoreMD21218 United States Department of Physics and Astronomy University of Rochester 500 Wilson Blvd RochesterNY14627 United States Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd LaurelMD20723 United States Department of Physics University of Cincinnati CincinnatiOH45221 United States Space Science Institute BoulderCO80301 United States Mit Department of Earth Atmospheric and Planetary Sciences Green Bldg The 77 Massachusetts Ave CambridgeMA02139 United States Aim Cea Cnrs Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité Gif-sur-YvetteF-91191 France Department of Physics and Astronomy University of North Carolina Chapel HillNC27599-3255 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91107 United States Center for Language and Speech Processing Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Computer Science and Engineering University of Michigan Ann ArborMI48109 United States

While β Pic is known to host silicates in ring-like structures, whether the properties of these silicate dust vary with stellocentric distance remains an open question. We re-analyze the β Pictoris debris disk spectrum from the Spitzer Infrared Spectrograph (IRS) and a new IRTF/SpeX spectrum to investigate trends in Fe/Mg ratio, shape, and crystallinity in grains as a function of wavelength, a proxy for stellocentric distance. By analyzing a recalibrated and re-extracted spectrum, we identify a new 18 μm forsterite emission feature and recover a 23 μm forsterite emission feature with a substantially larger line-to-continuum ratio than previously reported. We find that these prominent spectral features are primarily produced by small submicron-sized grains, which are continuously generated and replenished from planetesimal collisions in the disk and can elucidate their parent bodies' composition. We discover three trends about these small grains: as stellocentric distance increases, (1) small silicate grains become more crystalline (less amorphous), (2) they become more irregular in shape, and (3) for crystalline silicate grains, the Fe/Mg ratio decreases. Applying these trends to β Pic's planetary architecture, we find that the dust population exterior to the orbits of β Pic b and c differs substantially in crystallinity and shape. We also find a tentative 3-5 μm dust excess due to spatially unresolved hot dust emission close to the star. From our findings, we infer that the surfaces of large planetesimals are more Fe-rich and collisionally-processed closer to the star but more Fe-poor and primordial farther from the star. © 2022, CC BY.

关键词： Silicates

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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