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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Sakriani Sakti Noriyuki Kimura Michael Paul Chiori Hori Eiichiro Sumita Satoshi Nakamura Jun Park Chai Wutiwiwatchai Bo Xu Hammam Riza Karunesh Arora Chi Mai Luong Haizhou Li National Institute of Information and Communications Technology Japan Electronics and Telecommunications Research Institute South Korea National Electronics and Computer Technology Center Thailand Institute of Automation CASIA Chinese Academy and Sciences China Agency for Assessment and Application of Technology (BPPT) Indonesia Centre for Development of Advanced Computing India Institute of Information Technology IOIT Vietnam Institute for Infocomm Research Singapore

ISBN: (纸本)9781424454785

This paper outlines the first Asian network-based speech-to-speech translation system developed by the Asian Speech Translation advanced Research (A-STAR) consortium. The system was designed to translate common spoken utterances of travel conversations from a certain source language into multiple target languages in order to facilitate multiparty travel conversations between people speaking different Asian languages. Each A-STAR member contributes one or more of the following spoken language technologies: automatic speech recognition, machine translation, and text-to-speech through Web servers. Currently, the system has successfully covered 9 languages-namely, 8 Asian languages (Hindi, Indonesian, Japanese, Korean, Malay, Thai, Vietnamese, Chinese) and additionally, the English language. The system's domain covers about 20,000 travel expressions, including proper nouns that are names of famous places or attractions in Asian countries. In this paper, we discuss the difficulties involved in connecting various different spoken language translation systems through Web servers. We also present speech-translation results on the first A-STAR demo experiments carried out in July 2009.

关键词： Natural languages Automatic speech recognition Telecommunication computing Speech synthesis Web server Communications technology Computer networks Paper technology Automation Information technology

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Adversarial directed graph embedding

arXiv

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arXiv 2020年

作者： Zhu, Shijie Li, Jianxin Peng, Hao Wang, Senzhang He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node's source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

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A Blockchain-Based Collocation Storage Architecture for Data Security Process Platform of WSN

A Blockchain-Based Collocation Storage Architecture for Data...

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International Conference on Computer Supported Cooperative Work in Design

作者： Libo Feng Hui Zhang Liqi Lou Yong Chen State Key Laboratory of Software Development Environment Beihang University Beijing P.R. China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing P.R. China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing P.R. China

ISBN: (纸本)9781538614839

Wireless Sensor Networks (WSN) interconnects thousands of sensor nodes to support the services of Internet of Things (loT). However, data collected from sensor nodes may be tempered, forged and divulged. Traditional WSN data process platform handle the data centralized in terminal devices which is vulnerable to attacks and reduce the security of the system. Blockchain is a kind of distributed databases. It has been successfully applied in finance, securities, digital currency and other fields. We first propose a blockchain-based distributed collocation storage architecture for data security process platform of WSN with consensus protocol and asymmetric signature scheme. The security and efficiency are verified on the simulation to enable blockchain as a good solution.

关键词： Wireless sensor networks Encryption Distributed databases Public key Internet of Things

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Residual Networks for Light Field Image Super-Resolution

Residual Networks for Light Field Image Super-Resolution

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Shuo Zhang Youfang Lin Hao Sheng Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.

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Fine-grained event categorization with heterogeneous graph convolutional networks

arXiv

引用

arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Song, Yangqiu Ning, Yuanxing Lai, Kunfeng Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Department of Computer Science and Engineering Hong Kong University of Science and Technology Platform and Content Group Tencent Institute for Data Science Tsinghua University Department of Computer Science University of Illinois Chicago United States

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elements in texts and explicit and implicit social network structures. In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pairwise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model. We propose a Knowledgeable meta-paths Instances based social Event Similarity (KIES) between events and build a weighted adjacent matrix as input to the PP-GCN model. Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. Experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

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Quantitative gadolinium chelate-enhanced magnetic resonance imaging of normal endothelial barrier disruption from nanoparticle biophilicity interactions

引用

Materials Today: Proceedings 2021年 45卷 3795-3799页

作者： Hemant Sarin Steve H. Fung Ariel S Kanevsky Haitao Wu Colin M. Wilson Howard Vo Sungyoung Auh Daniel Glen Richard Reynolds Charleston WV USA Houston Methodist Hospital and Research Institute Houston TXUSA Jacobi Medical Center Bronx NY USA Imaging Probe Development Center Bethesda MD USA Department of Radiology University of New Mexico USA Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign USA National Institute of Neurologic Disorders and Stroke National Institutes of Health USA Scientific and Statistical Computing Core National Institute of Mental Helath National Institutes of Health USA

The biocompatibility of macromolecular therapeutics for enhanced passive permeation and retention remains a concern due to release kinetics or surface area properties. Dendritic conjugates functionalized with paramagnetic metal chelate with diameters less than (<) 12 nm and within the 8 to 10 nm size range can be utilized for high-resolution Gadolinium-based r 1 −1 -adjusted dynamic magnetic resonance concentration mapping of solid tumor barrier hyper-permeability and study of contrast agent pharmacodynamics. Cationic dye- or doxorubicin-linked dendrimer nanoparticle surface-to-membrane channel or receptor biophilicity interaction results in abnormal contrast enhancement of endothelial barriers identifiable by transvenous dual flip angle T 1 -weighted blood–brain barrier imaging with higher-generation polyamidoamine interior core gadopentetic acid-chelated dendrimers with 1 + IS 1 + functionalized exteriors. The neutralization of nanoparticle exterior surface charge would afford biocompatibility in clinical transability across abnormal barrier endothelium pores for effective transvascular delivery.

关键词： Theranostic agent Cationotoxicity Molecular diameter Chemoxenobiotic Vasomodulation General kinetic model Endocytic effective pressure

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Powerful Terahertz Emission from a Bi2Sr2CaCu2O8+δ Mesa Operating Above 77 K

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Physical Review Applied 2023年第3期19卷 034055-034055页

作者： K.J. Kihlstrom K.C. Reddy S. Elghazoly T.E. Sharma A.E. Koshelev U. Welp Y. Hao R. Divan M. Tsujimoto K. Kadowaki W.-K. Kwok T.M. Benseman Department of Physics Queens College of the City University of New York 6530 Kissena Blvd. Queens New York 11367 USA Materials Science Division Argonne National Laboratory Lemont Illinois 60439 USA Department of Physics University of Illinois at Chicago Chicago Illinois 60607 USA Center for Nanoscale Materials Argonne National Laboratory Lemont Illinois 60439 USA Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) Ibaraki 305-8768 Japan Algae Biomass and Energy System (ABES) Research & Development Center University of Tsukuba Ibaraki 305-8573 Japan

Mesa-shaped structures of the high critical temperature (high-Tc) superconductor Bi2Sr2CaCu2O8+δ contain stacked intrinsic Josephson junctions. As such, they are a promising source of coherent radiation in the “terahertz gap” range, spanning from approximately 0.3 to 2.0 THz. Technological applications of these devices become far more practical if they can be operated at a cryogenic bath temperature of 77 K or higher. Previous works have reported emission from this type of device at high terahertz power levels at lower operating temperatures, 40–60 K, while at Tbath≥77K observed power levels have generally been low. Here we report generation of 130 μW of coherent power at 0.456 THz from a mesa of Bi2Sr2CaCu2O8+δ doped with 0.16 holes per Cu atom, at a bath temperature of 77.4 K. We find that the device radiates terahertz power when clearly identifiable cavity modes are excited, and that the frequency and bias voltage corresponding to each of these modes is almost independent of temperature. This is consistent with these modes having terahertz-frequency electric fields with very little dependence on vertical position within the mesa. We also find that the terahertz power radiated from any given mode decreases monotonically as the mesa temperature is increased. On the other hand, the low-frequency modes become inaccessible at low temperatures due to retrapping of the intrinsic Josephson junctions, and the maximum radiation power for the emitting mode is typically achieved at the temperature at which the retrapping voltage reaches the resonance voltage for this mode.

关键词： Collective dynamics Josephson effect Optoelectronics d-wave Cuprates Josephson junctions Optical sources & detectors Terahertz sources

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A Distributed Privacy-Preserving Learning Dynamics in General Social Networks

arXiv

引用

arXiv 2020年

作者： Tao, Youming Chen, Shuzhen Li, Feng Yu, Dongxiao Yu, Jiguo Sheng, Hao School of Computer Science and Technology Shandong University Qingdao266237 China Big Data Institute Qilu University of Technology Jinan250353 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Rely-Guarantee Reasoning about Messaging System for Autonomous Vehicles

Rely-Guarantee Reasoning about Messaging System for Autonomo...

引用

Theoretical Aspects of Software Engineering (TASE)

作者： Wenjing Xu Yongwang Zhao Dianfu Ma YuXin Zhang Qian Xiao State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang Univerisity Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China State Key Laboratory of Automotive Simulation and Control College of Automotive Engineering Jilin University Jilin China School of Computer Science and Technology Guizhou University Guiyang China

Messaging system as a communicating infrastructure is a safety-critical component of autonomous vehicles. For the purpose of safety certification of Level 4 autonomous driving systems, its necessary to provide a formally verified specification of messaging systems. This paper presents a realistic case study using the PiCore rely-guarantee framework to formally verify the DGPS (Differential Global Positioning System) of UISEE autonomous driving systems. We first create an axiom model of messaging systems by extending PiCore, which is reusable for concrete applications. The model supports dynamic configuration of message buffers as well as the automatic generation of the rely and guarantee conditions for compositional reasoning. It is instantiated when developing the formal specification of DGPS. Then, we use the rely-guarantee proof system of PiCore to verify the functional correctness and invariant of DGPS. The specification and its proof provide a strong evidence for the ongoing safety certification of DGPS.

关键词： Cognition Safety Formal specifications Vehicle dynamics Autonomous vehicles Certification Global Positioning System

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Impact of temperature and relative humidity on the transmission of COVID-19: A modeling study in China and the United States

arXiv

引用

arXiv 2020年

作者： Wang, Jingyuan Tang, Ke Feng, Kai Lin, Xin Lv, Weifeng Chen, Kun Wang, Fei School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Social Sciences Tsinghua University China State Key Laboratory of Software Development Environment Beihang University China Department of Statistics University of Connecticut United States Center for Population Health University of Connecticut Health Center United States Department of Population Health Sciences Weill Cornell Medical College Cornell University United States

Objectives We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, and human mobility status. Design A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. Setting We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1,005 U.S. counties. Participants A total of 69,498 cases in China and 740,843 cases in the U.S. are used for calculating the effective reproductive numbers. Primary outcome measures Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). Results Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the U.S. Conclusions Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1 degree Celsius is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI [-0.0395,-0.0125]) in China and by 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.;an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI [-0.0108,-0.0045]) in China and by 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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