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arXiv 2019年

作者： Ruan, Yu-Ping Zhu, Xiaodan Ling, Zhen-Hua Shi, Zhan Liu, Quan Wei, Si National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei China Department of Electrical and Computer Engineering Queen's University KingstonON Canada iFLYTEK Research Hefei China

Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning. This paper presents the new state-of-theart on WSC, achieving an accuracy of 71.1%. We demonstrate that the leading performance benefits from jointly modelling sentence structures, utilizing knowledge learned from cutting-edge pretraining models, and performing fine-tuning. We conduct detailed analyses, showing that fine-tuning is critical for achieving the performance, but it helps more on the simpler associative problems. Modelling sentence dependency structures, however, consistently helps on the harder non-associative subset of WSC. Analysis also shows that larger fine-tuning datasets yield better performances, suggesting the potential benefit of future work on annotating more Winograd schema sentences. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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On the evaluation of semantic phenomena in neural machine translation using natural language inference

On the evaluation of semantic phenomena in neural machine tr...

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2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human language Technologies, NAACL HLT 2018

作者： Poliak, Adam Belinkov, Yonatan Glass, James Van Durme, Benjamin Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9781948087292

We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic *** use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring worldknowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage. © 2018 Association for Computational Linguistics.

关键词： Neural machine translation

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Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

SSRN

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SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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Espresso: A Fast End-to-End Neural speech Recognition Toolkit

Espresso: A Fast End-to-End Neural Speech Recognition Toolki...

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IEEE Workshop on Automatic speech Recognition and Understanding

作者： Yiming Wang Tongfei Chen Hainan Xu Shuoyang Ding Hang Lv Yiwen Shao Nanyun Peng Lei Xie Shinji Watanabe Sanjeev Khudanpur Center of Language and Speech Processing Johns Hopkins University Baltimore MD USA ASLP@NPU School of Computer Science Northwestern Polytechnical University Xian China Information Sciences Institute University of Southern California Los Angeles CA USA Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA

ISBN: (数字)9781728103068

ISBN: (纸本)9781728103075

We present Espresso, an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch and the popular neural machine translation toolkit FAIRSEQ. ESRESSO supports distributed training across GPUs and computing nodes, and features various decoding approaches commonly employed in ASR, including look-ahead word-based language model fusion, for which a fast, parallelized decoder is implemented. Espresso achieves state-of-the-art ASR performance on the WSJ, Librispeech, and Switchboard data sets among other end-to-end systems without data augmentation, and is 4-11x faster for decoding than similar systems (e.g. ESPNET).

关键词： Decoding speech recognition Training Computational modeling Gold Python Smoothing methods

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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A pilot study for virus-induced neural network remodeling in sudden hearing loss and tinnitus: A graph attention network approach

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computers in Biology and Medicine 2025年 194卷 110502页

作者： Fang, Chanmei Cui, Canming Wang, Chang-Dong Li, Jiahong Cai, Weiwei Chen, Hongyi Chen, Mansheng Song, Jae-Jin Chen, Yu-Chen Cai, Yuexin Liang, Maojin Department of Otolaryngology Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou China Institute of Hearing and Speech-Language Science Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Otolaryngology–Head and Neck Surgery The Affiliated Panyu Central Hospital of Guangzhou Medical University China Department of Department of Otolaryngology. Guangzhou Eighth People's Hospital Guangzhou Medical University Guangzhou China Department of Otorhinolaryngology-Head and Neck Surgery Seoul National University Bundang Hospital Seongnam Korea Republic of Department of Otorhinolaryngology-Head and Neck Surgery Seoul National University College of Medicine Seoul Korea Republic of Sensory Organ Research Institute Seoul National University Medical Research Center Seoul Korea Republic of Department of Radiology Nanjing First Hospital Nanjing Medical University No.68 Changle Road Nanjing210006 China

Purpose: This study aimed to investigate early central abnormalities in sudden sensorineural hearing loss (SSNHL) and tinnitus following viral infection, specifically associated with SARS-CoV-2. We sought to identify shared and distinct functional connectivity (FC) features across SSNHL and tinnitus patients with and without a history of SARS-CoV-2 infection and explore how virus influences brain network remodeling in SSNHL and tinnitus. Methods: We recruited 31 SSNHL patients with tinnitus following SARS-CoV-2 infection (COV-SSNHL), 32 non-viral SSNHL patients with tinnitus, and 32 age- and gender-matched healthy controls (HC). Brain functional connectivity coefficients were used to construct graph data analyzed with a graph attention network (GAT) model for categorization. Analyses focused on the auditory network (AN), salience network (SN), and default mode network (DMN) and their correlations with tinnitus severity. Results: Both SSNHL groups showed reduced connectivity within the SN and DMN, which correlated with tinnitus severity. Notably, COV-SSNHL patients exhibited distinct, complex network remodeling involving the AN, SN, and DMN, with the dorsal anterior cingulate cortex (dACC) as a central mediator. This remodeling facilitated interactions within the SN and across networks, marking COV-SSNHL as neurologically unique. Conclusion: This study highlights the distinct neural alterations in brain networks in COV-SSNHL patients, providing novel insights into the pathophysiology of viral-induced SSNHL and tinnitus. These findings suggest potential therapeutic targets and contribute to understanding the central mechanisms underlying these conditions. © 2025 The Authors

关键词： Coronavirus

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JOINT VERIFICATION-IDENTIFICATION IN END-TO-END MULTI-SCALE CNN FRAMEWORK FOR TOPIC IDENTIFICATION

JOINT VERIFICATION-IDENTIFICATION IN END-TO-END MULTI-SCALE ...

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IEEE International Conference on Acoustics, speech and Signal processing

作者： Raghavendra Pappagari Jesús Villalba Najim Dehak Center for Language and Speech Processing Department of Electrical and Computer Engineering The Johns Hopkins University USA

ISBN: (纸本)9781538646595

We present an end-to-end multi-scale Convolutional Neural Network (CNN) framework for topic identification (topic ID). In this work, we examined multi-scale CNN for classification using raw text input. Topical word embeddings are learnt at multiple scales using parallel convolutional layers. A technique to integrate verification and identification objectives is examined to improve topic ID performance. With this approach, we achieved significant improvement in identification task. We evaluated our framework on two contrasting datasets: 20 newsgroups and Fisher. We obtained 92.93% accuracy on Fisher and 86.12% on 20 newsgroups, which to our knowledge are the best published results on these datasets at the moment.

关键词： BOW raw text CNN verification identification topic id end-to-end BOW end-to-end Verification Martes pennanti newsgroups multi-scale Joints Dataset

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CrowdX: Enhancing automatic construction of indoor floorplan with opportunistic encounters

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第4期2卷 1–21页

作者： Chen, Huijie Li, Fan Hei, Xiaojun Wang, Yu School of Computer Science Beijing Institute of Technology Beijing Engineering Research Center High Volume Language Information Processing and Cloud Computing Applications Beijing China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Department of Computer Science University of North Carolina at Charlotte Charlotte United States

The lack of floorplan limits the spread of pervasive indoor location-based services. Existing crowdsourcing based approaches mostly rely on identifying, locating landmarks in the environment and utilizing the spatial relationship between the landmarks and traces for efficiently constructing fine-grained floorplan. However, these methods are always restricted by the sparse landmark distribution or may cause privacy leakage. In this paper, we propose CrowdX, a crowdsourcing system for accurate, low-cost indoor floorplan construction enhanced with opportunistic encounters among mobile users. The key insight is that the spatial relation (i.e., the displacement of each user and the distance between each other during the encounter) will be extracted from the audio and inertia data, which are aligned by the proposed vibration event-based method. Such information can be used to calibrate the drift of encounter position. The calibrated encounter position is beneficial to most of the floorplan generation steps, such as trace drift elimination, landmark positioning, hallway assembling, and room area estimation. Our experiments in three shopping malls show that CrowdX achieves an average F-measure around 89.4%. In addition, the average estimated room area error within about 20%. The evaluation results demonstrate a significant improvement of accuracy enhanced with opportunistic encounters. © 2018 Association for Computing Machinery.

关键词： Crowdsourcing

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Espresso: A fast end-to-end neural speech recognition toolkit

arXiv

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arXiv 2019年

作者： Wang, Yiming Chen, Tongfei Xu, Hainan Ding, Shuoyang Lv, Hang Shao, Yiwen Peng, Nanyun Xie, Lei Watanabe, Shinji Khudanpur, Sanjeev Center of Language and Speech Processing Johns Hopkins University BaltimoreMD United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States Information Sciences Institute University of Southern California Los AngelesCA United States ASLP@NPU School of Computer Science Northwestern Polytechnical University Xi'an China

We present ESPRESSO, an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch and the popular neural machine translation toolkit FAIRSEQ. ESPRESSO supports distributed training across GPUs and computing nodes, and features various decoding approaches commonly employed in ASR, including look-ahead word-based language model fusion, for which a fast, parallelized decoder is implemented. ESPRESSO achieves state-of-the-art ASR performance on the WSJ, Librispeech, and Switchboard data sets among other end-to-end systems without data augmentation, and is 4-11× faster for decoding than similar systems (e.g. ESPNET). Copyright © 2019, The Authors. All rights reserved.

关键词： speech recognition

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Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms

arXiv

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arXiv 2021年

作者： Poh, Norman Bourlai, Thirimachos Kittler, Josef Allano, Lorene Alonso-Fernandez, Fernando Ambekar, Onkar Baker, John Dorizzi, Bernadette Fatukasi, Omolara Fierrez, Julian Ganster, Harald Ortega-Garcia, Javier Maurer, Donald Salah, Albert Ali Scheidat, Tobias Vielhauer, Claus Centre for Vision Speech and Signal Processing School of Electronics and Physical Sciences University of Surrey Guildford SurreyGU2 7XH United Kingdom Biometrics Center Lane Department of Computer Science and Electrical Engineering College of Engineering and Mineral Resources West Virginia University MorgantownWV26506-6109 United States Institut Telecom Telecom and Management SudParis Evry91011 France Biometric Recognition Group – ATVS Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain Amsterdam1098 XG Netherlands Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States Electronics and Physics Department Institut Telecom Telecom and Management SudParis Evry91011 France Institute of Digital Image Processing Joanneum Research Graz8010 Austria ISLA-ISIS University of Amsterdam Amsterdam1098 XG Netherlands Department of Informatics and Media Brandenburg University of Applied Sciences Brandenburg an der HavelD-14770 Germany Research Group Multimedia and Security Department of Technical and Business Information Systems Faculty of Computer Science Otto-von-Guericke-University of Magdeburg MagdeburgD-39106 Germany

Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing

关键词： Benchmarking

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