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Overview of the Tenth Dialog System Technology Challenge: DSTC10

IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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Voice Conversion Augmentation for Speaker Recognition on Defective Datasets

arXiv

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arXiv 2024年

作者： Tao, Ruijie Shi, Zhan Jiang, Yidi Liu, Tianchi Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore School of Data Science The Chinese University of Hong Kong Shenzhen518172 China Singapore Institute of Technology Institute for Infocomm Research A∗STAR Singapore138632 Singapore Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen518172 China University of Bremen Bremen28359 Germany

Modern speaker recognition system relies on abundant and balanced datasets for classification training. However, diverse defective datasets, such as partially-labelled, small-scale, and imbalanced datasets, are common in real-world applications. Previous works usually studied specific solutions for each scenario from the algorithm perspective. However, the root cause of these problems lies in dataset imperfections. To address these challenges with a unified solution, we propose the Voice Conversion Augmentation (VCA) strategy to obtain pseudo speech from the training set. Furthermore, to guarantee generation quality, we designed the VCA-NN (nearest neighbours) strategy to select source speech from utterances that are close to the target speech in the representation space. Our experimental results on three created datasets demonstrated that VCA-NN effectively mitigates these dataset problems, which provides a new direction for handling the speaker recognition problems from the data aspect. © 2024, CC BY.

关键词： Classification (of information)

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SYNAPSE: An international roadmap to large brain imaging

引用

Physics Reports 2023年 999卷 1-60页

作者： Stampfl, Anton P.J. Liu, Zhongdong Hu, Jun Sawada, Kei Takano, H. Kohmura, Yoshiki Ishikawa, Tetsuya Lim, Jae-Hong Je, Jung-Ho Low, Chian-Ming Teo, Alvin Tok, Eng Soon Tan, Tin Wee Ban, Kenneth Libedinsky, Camilo Tan, Francis Chee Kuan Chen, Kuan-Peng Yang, An-Cheng Chuang, Chao-Chun Chen, Nan-Yow Shih, Chi-Tin Lee, Ting-Kuo Yang, De-Nian Lai, Hsu-Chao Shuai, Hong-Han Cheng, Chang-Chieh Ching, Yu-Tai Li, Chia-Wei Charng, Ching-Che Lo, Chung-Chuan Chiang, Ann-Shyn Recur, Benoit Petibois, Cyril Cheng, Chia-Liang Chen, Hsiang-Hsin Yang, Shun-Min Hwu, Yeukuang Rojviriya, Catleya Rugmai, Supagorn Rujirawat, Saroj Margaritondo, Giorgio Australian Nuclear Science and Technology Organization Lucas Heights 2234 NSW Australia Henan university of Technology Zhengzhou China Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai China RIKEN SPring-8 Center Hyogo 679-5148 Japan Pohang Accelerator Laboratory Pohang University of Science and Technology Pohang Gyeongbuk 37673 South Korea Department of Materials Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang 37673 South Korea Department of Pharmacology Department of Anaesthesia and Healthy Longevity Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore School of Chemical and Life Sciences Nanyang Polytechnic Singapore eMaGIC-Lab Department of Physics National University of Singapore Singapore National Supercomputing Center Singapore Department of Psychology The N1 Institute for Health National University of Singapore Singapore Institute of Molecular and Cell Biology A*STAR Singapore Department of Anaesthesia National University Hospital National University Health System Singapore National Center for High-Performance Computing Hsinchu Taiwan Department of Physics Tunghai University Taichung Taiwan Department of Physics National Tsing Hua University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Information Technology Service Center National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Life Science National Tsing Hua University Hsinchu Taiwan Institute of Systems Neuroscience and Brain Research Center National Tsing Hua University Hsinchu Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Physics National Dong Hwa University

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium. © 2022

关键词： Brain mapping Phase contrast Radiology Supercomputing Synchrotron X-rays

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Achievable Rate Maximization Pattern Design for Reconfigurable MIMO Antenna Array

arXiv

引用

arXiv 2022年

作者： Wang, Haonan Li, Ang Liu, Ya-Feng Qin, Qibo Song, Lingyang Li, Yonghui The School of Information and Communications Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China The State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Xi'An China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Wireless Network RAN Research Department Shanghai Huawei Technologies Co. Ltd. Shanghai201206 China The School of Electrical Engineering and Computer Science Peking University Beijing100871 China The School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia

Reconfigurable multiple-input multiple-output can provide performance gains over traditional MIMO by reshaping the channels, i.e., introducing more channel realizations. In this paper, we focus on the achievable rate maximization pattern design for reconfigurable MIMO systems. Firstly, we introduce the matrix representation of pattern reconfigurable MIMO (PR-MIMO), based on which a pattern design problem is formulated. To further reveal the effect of the radiation pattern on the wireless channel, we consider pattern design for both the single-pattern case where the optimized radiation pattern is the same for all the antenna elements, and the multi-pattern case where different antenna elements can adopt different radiation patterns. For the single-pattern case, we show that the pattern design is equivalent to a redistribution of gains among all scattering paths, and an eigenvalue optimization based solution is obtained. For the multi-pattern case, we propose a sequential optimization framework with manifold optimization and eigenvalue decomposition to obtain near-optimal solutions. Numerical results validate the superiority of PR-MIMO systems over traditional MIMO in terms of achievable rate, and also show the effectiveness of the proposed solutions. Copyright © 2022, The Authors. All rights reserved.

关键词： MIMO systems

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A novel CNN-LSTM-based approach to predict urban expansion

arXiv

引用

arXiv 2021年

作者： Boulila, Wadii Ghandorh, Hamza Khan, Mehshan Ahmed Ahmed, Fawad Ahmad, Jawad College of Computer Science and Engineering Taibah University Saudi Arabia RIADI Laboratory University of Manouba Tunisia Department of Electrical Engineering HITEC University Taxila Pakistan School of Computing Edinburgh Napier University United Kingdom

time-series remote sensing data offer a rich source of information that can be used in a wide range of applications, from monitoring changes in land cover to surveilling crops, coastal changes, flood risk assessment, and urban sprawl. This paper addresses the challenge of using time-series satellite images to predict urban expansion. Building upon previous work, we propose a novel two-step approach based on semantic image segmentation in order to predict urban expansion. The first step aims to extract information about urban regions at different time scales and prepare them for use in the training step. The second step combines Convolutional Neural Networks (CNN) with Long Short Term Memory (LSTM) methods in order to learn temporal features and thus predict urban expansion. In this paper, experimental results are conducted using several multi-date satellite images representing the three largest cities in Saudi Arabia, namely: Riyadh, Jeddah, and Dammam. We empirically evaluated our proposed technique, and examined its results by comparing them with state-of-the-art approaches. Following this evaluation, we determined that our results reveal improved performance for the new-coupled CNN-LSTM approach, particularly in terms of assessments based on Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy. © 2021, CC BY.

关键词： Long short-term memory

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Loss tangent fluctuations due to two-level systems in superconducting microwave resonators

arXiv

引用

arXiv 2024年

作者： Vallières, André Russell, Megan E. You, Xinyuan Garcia-Wetten, David A. Goronzy, Dominic P. Walker, Mitchell J. Bedzyk, Michael J. Hersam, Mark C. Romanenko, Alexander Lu, Yao Grassellino, Anna Koch, Jens McRae, Corey Rae H. Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Electrical Computing and Energy Engineering Department University of Colorado BoulderCO80309 United States Department of Physics University of Colorado BoulderCO80309 United States National Institute of Standards and Technology BoulderCO80305 United States

Superconducting microwave resonators are critical to quantum computing and sensing technologies. Additionally, they are common proxies for superconducting qubits when determining the effects of performance-limiting loss mechanisms such as from two-level systems (TLS). The extraction of these loss mechanisms is often performed by measuring the internal quality factor Qi as a function of power or temperature. In this work, we investigate large temporal fluctuations of Qi at low powers over periods of 12 to 16 hours (relative standard deviation σQi/Qi = 13%). These fluctuations are ubiquitous across multiple resonators, chips and cooldowns. We are able to attribute these fluctuations to variations in the TLS loss tangent due to two main indicators. First, measured fluctuations decrease as power and temperature increase. Second, for interleaved measurements, we observe correlations between low- and medium-power Qi fluctuations and an absence of correlations with high-power fluctuations. Agreement with the TLS loss tangent mean is obtained by performing measurements over a time span of a few hours. We hypothesize that, in addition to decoherence due to coupling to individual near-resonant TLS, superconducting qubits are affected by these observed TLS loss tangent fluctuations. © 2024, CC BY.

关键词： Qubits

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The SMART BEAR Project: An Overview of Its Infrastructure 7th

The SMART BEAR Project: An Overview of Its Infrastructure

引用

7th and 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2021 and ICT4AWE 2022

作者： Su, Qiqi Peretokin, Vadim Basdekis, Ioannis Kouris, Ioannis Maggesi, Jonatan Sicuranza, Mario Acebes, Alberto Bucur, Anca Mukkala, Vinod Jaswanth Roy Pozdniakov, Konstantin Kloukinas, Christos Koutsouris, Dimitrios D. Iliadou, Elefteria Leontsinis, Ioannis Gallo, Luigi De Pietro, Giuseppe Spanoudakis, George Department of Computer Science City University of London London United Kingdom Philips Research Eindhoven Netherlands SPHYNX Technology Solutions AG Zug Switzerland Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Computer Science Department Università degli Studi di Milano Milan Italy Institute for High-Performance Computing and Networking National Research Council of Italy ICAR - CNR Naples Italy Atos Research and Innovation Madrid Spain 1st Otolaryngology University Department National and Kapodistrian University of Athens Athens Greece 1st Cardiology Clinic Medical School National and Kapodistrian University of Athens Athens Greece

ISBN: (纸本)9783031374951

The paper describes a cloud-based platform that utilizes Artificial Intelligence (AI) and Explainable AI techniques to deliver evidence-based, personalized interventions to individuals over 65 suffering or at risk of hearing loss, cardiovascular disease, cognitive impairments, balance disorders, or mental health issues, while supporting efficient remote monitoring and clinician-driven guidance. As part of the SMART BEAR integrated project, this platform has been developed to support its large-scale clinical trials. The platform consists of a standards-based data harmonization and management layer, as well as a security component, a Big Data Analytics system, a Clinical Decision Support system, and a dashboard component to facilitate efficient data collection across pilot sites. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Vertical Federated Learning: Challenges, Methodologies and Experiments

arXiv

引用

arXiv 2022年

作者： Wei, Kang Li, Jun Ma, Chuan Ding, Ming Wei, Sha Wu, Fan Chen, Guihai Ranbaduge, Thilina School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Ministry of Education China Data61 CSIRO SydneyNSW2015 Australia Beijing100191 China The Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Recently, federated learning (FL) has emerged as a promising distributed machine learning (ML) technology, owing to the advancing computational and sensing capacities of end-user devices, as well as the increasing concerns on users' privacy. As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different types of clients. These sub-models are trained locally by vertically partitioned data with distinct attributes. Therefore, the design of VFL is fundamentally different from that of conventional FL, raising new and unique research issues. In this paper, we aim to discuss key challenges in VFL with effective solutions, and conduct experiments on real-life datasets to shed light on these issues. Specifically, we first propose a general framework on VFL, and highlight the key differences between VFL and conventional FL. Then, we discuss research challenges rooted in VFL systems from four aspects, i.e., security and privacy risks, expensive computation and communication costs, possible structural damage caused by model splitting, and system heterogeneity. Afterwards, we develop solutions to addressing the aforementioned challenges, and conduct extensive experiments to showcase the effectiveness of the proposed solutions. Copyright © 2022, The Authors. All rights reserved.

关键词： Federated learning

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INTERACTIVE MODEL WITH STRUCTURAL LOSS FOR LANGUAGE-BASED ABDUCTIVE REASONING

arXiv

引用

arXiv 2021年

作者： Li, Linhao Xu, Ming Dong, Yongfeng Li, Xin Wang, Ao The School of Artificial Intellgence The Hebei Provincial Key Laboratory of Big Data Computing Hebei University of Technology China The Lane Department of Computer Science and Electrical Engineering West Virginia University United States

The abductive natural language inference task (αNLI) is proposed to infer the most plausible explanation between the cause and the event. In the αNLI task, two observations are given, and the most plausible hypothesis is asked to pick out from the candidates. Existing methods model the relation between each candidate hypothesis separately and penalize the inference network uniformly. In this paper, we argue that it is unnecessary to distinguish the reasoning abilities among correct hypotheses;and similarly, all wrong hypotheses contribute the same when explaining the reasons of the observations. Therefore, we propose to group instead of ranking the hypotheses and design a structural loss called "joint softmax focal loss" in this paper. Based on the observation that the hypotheses are generally semantically related, we have designed a novel interactive language model aiming at exploiting the rich interaction among competing hypotheses. We name this new model for αNLI: Interactive Model with Structural Loss (IMSL). The experimental results show that our IMSL has achieved the highest performance on the RoBERTa-large pretrained model, with ACC and AUC results increased by about 1% and 5% respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Deep Learning Methods for Calibrated Photometric Stereo and Beyond

arXiv

引用

arXiv 2022年

作者： Ju, Yakun Lam, Kin-Man Xie, Wuyuan Zhou, Huiyu Dong, Junyu Shi, Boxin School of Electrical and Electronic Engineering Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong The Research Institute for Future Media Computing Shenzhen University Shenzhen China The Department of Informatics University of Leicester Leicester United Kingdom The Faculty of Information Science and Engineering The Institute for Advanced Ocean Study Ocean University of China Qingdao China The National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models. Copyright © 2022, The Authors. All rights reserved.

关键词： Light sources

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