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IEEE Conference on Vehicular Technology (VTC)

作者： Imad Ali Ronald Y. Chang Cheng-Hsin Hsu Chi-Han Lee Taiwan International Graduate Program in Social Networks and Human-Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

Recent research has shown that people are frequently searching for information with local intent. The existing systems fail to answer this class of questions as they are not mainly designed to handle it. In this paper, we propose LOCI, a mobile question & answer system with a multimodal motivation scheme for local intent questions where users provide timely and accurate answers in a dynamic social network. LOCI mines users' shared and contextual information to identify the most relevant users for each question. The proposed multimodal motivation scheme exploits users' social ties and monetary rewards to raise their basic motivation levels. LOCI assigns questions to the best answerers via our three proposed algorithms: (i) LOCI-RT, which immediately assigns newly arrived questions/users to available users/questions, (ii) LOCI-LO, which uses short-time batches for locally optimal questions-users assignment, and (iii) LOCI-AD, which assigns short- and long-deadline questions immediately and via batching, respectively. We conduct: (i) a survey to support our assumptions regarding the proposed multimodal motivation scheme, and (ii) trace-driven experiments to evaluate the performance of the three proposed LOCI's algorithms.

关键词： Social networking (online) Resource management Heuristic algorithms Real-time systems Computer science Search engines Servers

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Mixing-specific data augmentation techniques for improved blind violin/piano source separation

arXiv

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arXiv 2020年

作者： Chiu, Ching-Yu Hsiao, Wen-Yi Yeh, Yin-Cheng Yang, Yi-Hsuan Su, Alvin Wen-Yu Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan Dept. CSIE National Cheng Kung University Taiwan

Blind music source separation has been a popular and active subject of research in both the music information retrieval and signal processing communities. To counter the lack of available multi-track data for supervised model training, a data augmentation method that creates artificial mixtures by combining tracks from different songs has been shown useful in recent works. Following this light, we examine further in this paper extended data augmentation methods that consider more sophisticated mixing settings employed in the modern music production routine, the relationship between the tracks to be combined, and factors of silence. As a case study, we consider the separation of violin and piano tracks in a violin piano ensemble, evaluating the performance in terms of common metrics, namely SDR, SIR, and SAR. In addition to examining the effectiveness of these new data augmentation methods, we also study the influence of the amount of training data. Our evaluation shows that the proposed mixing-specific data augmentation methods can help improve the performance of a deep learning-based model for source separation, especially in the case of small training data. Copyright © 2020, The Authors. All rights reserved.

关键词： Mixing

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Fixed-wing UAV robust guidance strategy based on compound flight mission of on-ground targets 31

Fixed-wing UAV robust guidance strategy based on compound fl...

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31st Congress of the International Council of the Aeronautical Sciences, ICAS 2018

作者： Olavo, José Lucas Gomes Torres, Leonardo Antônio Borges Jesus, Tales Argolo Graduate Program in Electrical Engineering UFMG Brazil Department of ElectronicsEngineering UFMG Brazil Department of Computing CEFET Brazil

ISBN: (纸本)9783932182884

A controlled fixed wing unmanned aerial vehicle is modeled based on a simplified low dimensional nonlinear dynamical system with additive disturbances. The guidance strategy proposed on a previous work is extended for the accomplishment of more complex flight missions. The trajectory is composed by subsequent targets or the concatenation of arcs of circumferences that compose the goal mission. The strategy seems to be robust to additive perturbations representing norm bounded uncertainties on the execution of the compound mission by the UAV. Simulations are provided on area mapping and border patrol missions on the map of the city of Belo Horizonte. © 31st Congress of the International Council of the Aeronautical Sciences, ICAS 2018. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Self-Supervised Denoising Autoencoder with Linear Regression Decoder for Speech Enhancement

Self-Supervised Denoising Autoencoder with Linear Regression...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Ryandhimas E. Zezario Tassadaq Hussain Xugang Lu Hsin-Min Wang Yu Tsao Research Center for Information Technology Innovation Academia Sinica Taiwan Taiwan International Graduate Program in Social Network and Human-Centered Computing Institute of Information Science Academia Sinica Taiwan National Institute of Information and Communications Technology Japan Institute of Information Science Academia Sinica Taiwan

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Nonlinear spectral mapping-based models based on supervised learning have successfully applied for speech enhancement. However, as supervised learning approaches, a large amount of labelled data (noisy-clean speech pairs) should be provided to train those models. In addition, their performances for unseen noisy conditions are not guaranteed, which is a common weak point of supervised learning approaches. In this study, we proposed an unsupervised learning approach for speech enhancement, i.e., denoising autoencoder with linear regression decoder (DAELD) model for speech enhancement. The DAELD is trained with noisy speech as both input and target output in a self-supervised learning manner. In addition, with properly setting a shrinkage threshold for internal hidden representations, noise could be removed during the reconstruction from the hidden representations via the linear regression decoder. Speech enhancement experiments were carried out to test the proposed model. Results confirmed that the proposed DAELD could achieve comparable and sometimes even better enhancement performance as compared to the conventional supervised speech enhancement approaches, in both seen and unseen noise environments. Moreover, we observe that higher performances tend to achieve by DAELD when the training data cover more diverse noise types and signal-tonoise-ratio (SNR) levels.

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Are Electric Vehicle Stocks in ASEAN Countries Investible during the Covid-19 Pandemic?

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IOP Conference Series: Earth and Environmental Science 2022年第1期997卷

作者： Christy Dwita Mariana Harry Patria Graduate Program of Business Management Department of Management BINUS Business School Bina Nusantara University Jakarta 10270 Indonesia PT. Strategi Transforma Infiniti Jakarta Indonesia School of Computing Newcastle University Newcastle Upon Tyne NE1 7RU United Kingdom

Electric vehicle, as one of the low carbon footprints, has transformed a wide range of energy and public sectors. Utilizing the WHO Covid-19 pandemic statement, we search for the optimal portfolio from the top seven Electric Vehicle (EV) enterprises in ASEAN. Using the MonteCarlo simulation of optimal risky assets portfolio, we find that before and during the pandemic, the optimal portfolio weights differ significantly. Before (during) the pandemic, the investment weight of optimal portfolio consists of Toyota Motor Corporation 49.83% (42.99%), Star 8 Corporation 23.20% (9.73%), Hyundai Motor Corporation 20.81% (10.63%), *** 2.79% (32.08%), Honda Motor Company 2.56% (0.64%), Mitsubishi Motor Corporation 0.68% (0.09%), and Nissan Motor Co. 0.14% (3.84%). The Sharpe Ratio shows how during the pandemic, the portfolio of EV stocks give more excessive return compare to their risks, from 93.55% to 250.62%. These findings support the notion of how EV stocks are investible, especially during the Covid-19 pandemic.

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User interface modelling by implementing storytelling on Sundanese cultural introduction media for early childhood using child-centered design method 3

User interface modelling by implementing storytelling on Sun...

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3rd Annual Applied Science and Engineering Conference, AASEC 2018

作者： Ayuningtyas, O.D.J. Effendy, V. Kaburuan, E.R. School of Computing Universitas Telkom Jawa Barat Bandung Indonesia Graduate Program Universitas Bina Nusantara Jakarta Indonesia

The introduction of local culture to early age children in Indonesia is very necessary to maintain its sustainability. Several institutions of Early Childhood Education began to introduce local culture to their students, but the limited time and place to be a separate constraint for the activity. Digital media can solve this problem by presenting an introduction to local culture which can be accessed without time and place limitation, but there are some problems of using the existing application because children have their own characteristics and different ways of absorbing a knowledge. From the observation of an early childhood, it is known that children more easily absorb knowledge through playing game and listening to the story telling. Story telling invites children to develop their imagination and more memorable, so that information can be better absorbed. To accommodate the learning need, this research developed the User Interface (UI) by combining a game and a story telling. This modelling involves the participation of early childhood and companions using child-centered design (CCD) methods. The UI model then tested using System Usability Scale (SUS) to measure its usability score and obtained 85.833, which means the children feel satisfied in using the UI model. © Published under licence by IOP Publishing Ltd.

关键词： Digital storage

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Identifying Materials-Level Sources of Performance Variation in Superconducting Transmon Qubits

arXiv

引用

arXiv 2025年

作者： Murthy, Akshay A. Bal, Mustafa Bedzyk, Michael J. Cansizoglu, Hilal Chan, Randall K. Chandrasekhar, Venkat Crisa, Francesco Datta, Amlan Deng, Yanpei Matute Diaz, Celeo D. Dravid, Vinayak P. Garcia-Wetten, David A. Garattoni, Sabrina Ghimire, Sunil Goronzy, Dominic P. de Graaf, Sebastian E. Haeuser, Sam Hersam, Mark C. Isheim, Dieter Joshi, Kamal Kim, Richard Kolachina, Saagar Kopas, Cameron J. Kramer, Matthew J. Lachman, Ella O. Lee, Jaeyel Lim, Peter G. Lunin, Andrei Mah, William Marshall, Jayss Mutus, Josh Y. Oh, Jin-Su Pappas, David P. Park, Joong-Mok Prozorov, Ruslan dos Reis, Roberto Seidman, David N. Sung, Zuhawn Tanatar, Makariy Walker, Mitchell J. Wang, Jigang Wu, Haotian Zhou, Lin Zhu, Shaojiang Grassellino, Anna Romanenko, Alexander BataviaIL60510 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Rigetti Computing BerkeleyCA94710 United States Ames National Laboratory AmesIA50011 United States Department of Physics & Astronomy Iowa State University AmesIA50011 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States The NUANCE Center Northwestern University EvanstonIL60208 United States International Institute of Nanotechnology Northwestern University EvanstonIL60208 United States National Physical Laboratory TeddingtonTW11 0LW United Kingdom Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Applied Physics Graduate Program Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Iowa State University AmesIA50011 United States

The Superconducting Materials and Systems (SQMS) Center, a DOE National Quantum Information Science Research Center, has conducted a comprehensive and coordinated study using superconducting transmon qubit chips with known performance metrics to identify the underlying materials-level sources of device-to-device performance variation. Following qubit coherence measurements, these qubits of varying base superconducting metals and substrates have been examined with various nondestructive and invasive material characterization techniques at Northwestern University, Ames National Laboratory, and Fermilab as part of a blind study. We find trends in variations of the depth of the etched substrate trench, the thickness of the surface oxide, and the geometry of the sidewall, which when combined, lead to correlations with the T1 lifetime across different devices. In addition, we provide a list of features that varied from device to device, for which the impact on performance requires further studies. Finally, we identify two low-temperature characterization techniques that may potentially serve as proxy tools for qubit measurements. These insights provide materials-oriented solutions to not only reduce performance variations across neighboring devices, but also to engineer and fabricate devices with optimal geometries to achieve performance metrics beyond the state-of-the-art values. © 2025, CC BY.

关键词： Qubits

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Inter-comparison of multiple Global Climate Model (GCM) data based on spatial pattern of rainfall over Indonesia 5

Inter-comparison of multiple Global Climate Model (GCM) data...

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5th International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring 2018, LISAT-FSEM 2018

作者： Aflahah, Elania Latifah, Arnida Lailatul Hidayat, Rahmat Hidayati, Rini Ihwan, Andi Applied Climatology Study Program Graduate School Bogor Agricultural University Bogor16680 Indonesia Laboratorium of High-Performance Computing Research Center for Informatics Indonesian Institute of Sciences West Java Bogor16916 Indonesia Department of Geophysics and Meteorology Bogor Agricultural University Bogor16680 Indonesia

The Coupled Model Inter-comparison Project Phase 5 (CMIP5) is the output of many coupled atmosphere-ocean of global climate models (GCMs) and widely used for climate research, especially for driving regional climate model. There are more than 40 CMIP5 GCMs data available, but no single model can be considered as the best for every region. The use of CMIP5 GCMs data for rainfall projection in Indonesia is important to improve the accuracy of the monthly and seasonal rainfall forecast. Then, this study evaluates the capability of the CMIP5 GCMs data for Indonesia region by quantitatively comparing the spatial pattern of the precipitation mean and standard deviation of the CMIP5 data against GPCP, GPCC, and CRU data in the period 1980-2005. Furthermore, the composite analysis is conducted to observe the model performance in reproducing the precipitation characteristic over some areas in Indonesia. In conclusion, the models NorESM1-M, NorESM1-ME, GFDL-ESM2M, CSIRO-MK3-6-0 perform the rainfall mean better than others, while the standard deviation of the rainfall show that the models NorESM1-M, BNU-ESM, CMCC-CMS are superior in which NorESM1-M gives the best performance. The annual precipitation pattern of the model NorESM1-M over various areas in Indonesia is also highly correlated with the observations. Thus, the most suitable model for Indonesia region is NorESM1-M. © Published under licence by IOP Publishing Ltd.

关键词： Climate models

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A proposal for postpartum support based on natural language generation model

A proposal for postpartum support based on natural language ...

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2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018

作者： Zeni Montenegro, Joao Luis Andre Da Costa, Cristiano Da Rosa Righi, Rodrigo Roehrs, Alex Farias, Elson Romeu SOFTWARELAB Applied Computing Graduate Program Universidade Do Vale Do Rio Dos Sinos Sao Leopoldo Brazil

ISBN: (纸本)9781728113609

This article presents a Multifaceted Natural Language Generation Model to help women in a postpartum. This proposal supports speech and text interactions, developing responses through an ontology model and retrieval based-method techniques oriented to the welfare of women after the birth of their baby. We show a case study, introducing an architecture model of conversational agents, a scenario and a simulation of this proposal. Our results presented a better performance in response time generated from a retrieval based-method(two seconds), and similar response accuracy, slightly better for semantic queries (20% better). © 2018 IEEE.

关键词： Ontology

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Transfer learning approach to modeling multichannel gate-all-around nanosheet field-effect transistors under work function fluctuations

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Engineering Applications of Artificial Intelligence 2025年 148卷

作者： Dash, Sagarika Li, Yiming Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Artificial Intelligence Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Microelectronics National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan

Deep learning (DL) has significantly advanced various industries, including semiconductors, by providing sophisticated methods for analyzing emerging device data. Transfer learning (TL), a prominent DL topology, leverages knowledge from a pre-trained model to improve the performance of a specific target task. This study aims to apply TL to predict the impact of work function fluctuation (WKF) on the variability of threshold voltage (σVTH) in gate-all-around silicon nanosheet field-effect transistors (GAA Si NS FETs). We used TL to transfer knowledge from models trained on 1-channel (1ch) GAA Si NS FETs to more complex 4-channel (4ch) counterparts. The prediction accuracy is evaluated by using the mean square error(MSE), root mean square error (RMSE), and the mean absolute error(MAE) metrics. Our TL approach achieved an error rate of less than 1%, demonstrating high accuracy in predicting WKF effects on σ VTH of 4ch GAA Si NS FETs using only 500 samples. This study presents a pioneering application of TL using a one-dimensional (1D) convolutional neural network (CNN) long short-term memory (LSTM) topology in semiconductor device technology. The results effectively address the challenge of predicting σ VTH variations in advanced GAA Si NS FETs because of WKF, which eliminates the need for extensive semiconductor device simulations. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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