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International Symposium on Chinese Spoken Language Processing

作者： Kun-Yi Huang Chung-Hsien Wu Qian-Bei Hong Ming-Hsiang Su Yuan-Rong Zeng Department of Computer Science and Information Engineering National Cheng Kung University Taiwan PhD Program for Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

ISBN: (纸本)9781538656280;9781538656273

A complete emotional expression typically contains a complex temporal course in a natural conversation. Related research on utterance-level, segment-level and multi-level processing lacks understanding of the underlying relation of emotional speech. In this work, a convolutional neural network (CNN) with audio word-based embedding is proposed for emotion modeling. In this study, vector quantization is first applied to convert the low level features of each speech frame into audio words using k-means algorithm. Word2vec is adopted to convert an input speech utterance into the corresponding audio word vector sequence. Finally, the audio word vector sequences of the training emotional speech data with emotion annotation are used to construct the CNN- based emotion model. The NCKU-ES database, containing seven emotion categories: happiness, boredom, anger, anxiety, sadness, surprise and disgust, was collected and five-fold cross validation was used to evaluate the performance of the proposed CNN-based method for speech emotion recognition. Experimental results show that the proposed method achieved an emotion recognition accuracy of 82.34%, improving by 8.7% compared to the Long Short Term Memory (LSTM)- based method, which faced the challenging issue of long input sequence. Comparing with raw features, the audio word-based embedding achieved an improvement of 3.4% for speech emotion recognition.

关键词： Emotion recognition Feature extraction Speech recognition Acoustics Training Databases Neural networks

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Sketching a Generative Model of Intention Management for Characters In Stories: Adding Intention Management to a Belief-Driven Story Planning Algorithm

Sketching a Generative Model of Intention Management for Cha...

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Workshops of the 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2017

作者： Young, R. Michael School of Computing The Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

Previous work on story planning has shown success in the generation of plans that are both intention-coherent and demonstrate aspects of inter-character conflict. However, the initial models of intention and conflict have been limited, in that they lack methods to generate story plots where characters drop sub-plans to achieve their goals in believably consistent and expressive ways and adopt new sub-plans in the face of plan failure. In current work, we have developed models of failed actions in stories that go hand in hand with erroneous belief models for character. Motivated by characterizations of rational agents' intentions as choice combined with commitment, we provide a framing of the plan generation process that is intended to show how characters form their own plans to achieve their own goals, act upon those plans until they feel that conditions no longer support their plans, and then re-plan in the face of adversity to achieve their goals. We show an example story plan that contains several types of character-based intention dynamics targeted by our approach. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Generative adversarial networks

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Exploring Macroscopic Fluctuation of Facial Expression for Mood Disorder Classification

Exploring Macroscopic Fluctuation of Facial Expression for M...

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Asian Conference on Affective computing and Intelligent Interaction (ACII Asia)

作者： Qian-Bei Hong Chung-Hsien Wu Ming-Hsiang Su Kun-Yi Huang Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781538653128

In clinical diagnosis of mood disorder, a large portion of bipolar disorder patients (BDs) are misdiagnosed as unipolar depression (UDs). Clinicians have confirmed that BDs generally show "reduced affect'' during clinical treatment. Thus, it is expected to build an objective and one-time diagnosis system for diagnosis assistance by using machine-learning techniques. In this study, facial expressions of BD, UD and control group (C) elicited by emotional video clips are collected for exploring temporal fluctuation characteristics of intensities of facial muscles expression among the three groups. The differences of facial expressions among mood disorders are investigated by observing macroscopic fluctuations. To deal with these problems, the corresponding methods for feature extraction and modeling are proposed. From the viewpoint of macroscopic facial expression, action unit (AU) is applied for describing the temporal transformation of muscles. Then, modulation spectrum is used for extracting short-term variation of AU. The multilayer perceptron (MLP)-based disorder prediction model is then applied to obtain the prediction results. For evaluation of the proposed method, 12 subjects for three group are included in the K-fold (K=12) cross validation experiments. The experiment results reached 61.1% classification accuracy, and outperformed the other baseline methods.

关键词： Mood Gold Fluctuations Databases Feature extraction Modulation Predictive models

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LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors

LSTM-based Text Emotion Recognition Using Semantic and Emoti...

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Asian Conference on Affective computing and Intelligent Interaction (ACII Asia)

作者： Ming-Hsiang Su Chung-Hsien Wu Kun-Yi Huang Qian-Bei Hong Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan

ISBN: (纸本)9781538653128

This study proposes a long-short term memory (LSTM)-based approach to text emotion recognition based on semantic word vector and emotional word vector of the input text. For each word in an input text, the semantic word vector is extracted from the word 2vec model. Besides, each lexical word is projected to all the emotional words defined in an affective lexicon to derive an emotional word vector. An autoencoder is then adopted to obtain the bottleneck features from the emotional word vector for dimensionality reduction. The autoencoder bottleneck features are then concatenated with the features in the semantic word vector to form the final textual features for emotion recognition. Finally, given the textual feature sequence of the entire sentence, the LSTM is used for emotion recognition by modeling the contextual emotion evolution of the input text. For evaluation, the NLPCC-MHMC-TE database containing seven emotion categories: anger, boredom, disgust, anxiety, happiness, sadness, and surprise was constructed and used. Five-fold cross-validation was employed to evaluate the performance of the proposed method. Experimental results show that the proposed LSTM-based method achieved a recognition accuracy of 70.66%, improving 5.33% compared with the CNN-based method. Besides, the proposed method based on integration of the semantic word vector and emotional word vector of the input text outperformed that using the individual feature vector.

关键词： Emotion recognition Databases Logic gates Feature extraction Semantics Speech recognition Computer science

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Syncgan: Synchronize the Latent Spaces of Cross-Modal Generative Adversarial Networks

Syncgan: Synchronize the Latent Spaces of Cross-Modal Genera...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wen-Cheng Chen Chien-Wen Chen Min-Chun Hu Dept. of Computer Science and Information Engineering National Cheng Kung University Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

Generative adversarial network (GAN) has achieved impressive success on cross-domain generation, but it faces difficulty in cross-modal generation due to the lack of a common distribution between heterogeneous data. Most existing methods of conditional based cross-modal GANs adopt the strategy of one-directional transfer and have achieved preliminary success on text-to-image transfer. Instead of learning the transfer between different modalities, we aim to learn a synchronous latent space representing the cross-modal common concept. A novel network component named synchronizer is proposed in this work to judge whether the paired data is synchronous/corresponding or not, which can constrain the latent space of generators in the GANs. Our GAN model, named as SyncGAN, can successfully generate synchronous data (e.g., a pair of image and sound) from identical random noise. For transforming data from one modality to another, we recover the latent code by inverting the mappings of a generator and use it to generate data of different modality. In addition, the proposed model can achieve semi-supervised learning, which makes our model more flexible for practical applications.

关键词： Synchronization Gallium nitride Generators Data models Training Generative adversarial networks Training data

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Biofuels in Malaysian perspective: Debates and benefits

Biofuels in Malaysian perspective: Debates and benefits

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Compatibility in Power Electronics (CPE)

作者： Md Bazlul Mobin Siddique Saad Bin Abul Kashem Atif Iqbal School of Engineering Computing and Sciences Swinburne University of Technology Sarawak Kuching Malaysia Computer Skills Faculty Teacher Qatar Armed Forces - Academic Bridge Program Qatar Foundation Doha Qatar Electrical Engineering Qatar University Doha Qatar

Fossil fuels bring about concerns like air pollution, rising greenhouse gas emissions due to its combustion and depletion issues. The need to more to biofuels as an alternative and renewable energy source is for a sustainable and environment for the future. This research project studies the biofuels in Malaysian perspective, the debates concerning the issues as well as the advantages and disadvantages of biofuels. The production of biofuels and comparison with fossil fuels has been done based on the social, economic and environmental context. Research is also conducted on the organizations producing biofuels in Malaysia, volume of fossil fuels are being currently replaced by biofuels, and where the projection in the next 20 years will be. After all the studies and observations are carried out, personal evaluation of further requirements or developments is made. It can be concluded that Malaysia will not be able to replace its fossil fuels with biofuels in the near future.

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Multiwavelength study of GRB 201216C using the sub-TeV emission detected by MAGIC 38

Multiwavelength study of GRB 201216C using the sub-TeV emiss...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Fukami, Satoshi Berti, Alessio Loporchio, Serena Nava, Lara Suda, Yusuke Noda, Koji Asano, Katsuaki Bošnjak, Željka Longo, Francesco Jordana-Mitjans, Nuria Melandri, Andrea Mundell, Carole Shrestha, Manisha Steele, Iain Abe, H. Abe, S. Abhir, J. Acciari, V.A. Agudo, I. Aniello, T. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Arcaro, C. Artero, M. Asano, K. Baack, D. Babić, A. Baquero, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Baxter, J. Becerra González, J. Bednarek, W. Bernardini, E. Bernardos, M. Bernete, J. Berti, A. Besenrieder, J. Bigongiari, C. Biland, A. Blanch, O. Bonnoli, G. Bošnjak, Ž. Burelli, I. Busetto, G. Campoy-Ordaz, A. Carosi, A. Carosi, R. Carretero-Castrillo, M. Castro-Tirado, A.J. Ceribella, G. Chai, Y. Chilingarian, A. Cifuentes, A. Cikota, S. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D’Amico, G. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Del Popolo, A. Delfino, M. Delgado, J. Delgado Mendez, C. Depaoli, D. Di Pierro, F. Di Venere, L. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Emery, G. Escudero, J. Fariña, L. Fattorini, A. Foffano, L. Font, L. Fröse, S. Fukami, S. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giesbrecht Paiva, J.G. Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Grau, R. Green, D. Green, J.G. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera, J. Hrupec, D. Hütten, M. Imazawa, R. Inada, T. Iotov, R. Ishio, K. Jiménez Martínez, I. Jormanainen, J. Kerszberg, D. Kluge, G.W. Kobayashi, Y. Kouch, P.M. Kubo, H. Kushida, J. Láinez Lezáun, M. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Lorini, A. Lyard, E. Machado de Oliveira Fraga, B. Majumdar, P. Makariev, M. Maneva, G. Mang, N. Manganaro, M. Mangano, S. Mannheim, K. Mariotti, M. Martínez, M. Martínez-Chicharro, M. Mas-Aguilar, A. Mazin, D. Menchiari, S. Mender, S. Institute for Particle Physics and Astrophysics ETH Zürich Otto-Stern-Weg 5 Zürich8093 Switzerland Institute for Cosmic Ray Research The University of Tokyo Kashiwanoha 5-1-5 Kashiwa Japan Max Planck Institut for Physics Föhringer Ring 6 Munich Germany INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica Università e del Politecnico di Bari BariI-70125 Italy RomeI-00136 Italy Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia INFN Sezione di Trieste via Valerio 2 Trieste Italy Department of Physics University of Bath Claverton Down BathBA2 7AY United Kingdom INAF-Osservatorio Astronomico di Brera Via E. Bianchi 46 LC MerateI-23807 Italy European Space Agency European Space Astronomy Centre Villanueva de la Canñada Madrid28692 Spain Astrophysics Research Institute Liverpool John Moores University Liverpool Science Park IC2 146 Brownlow HillL3 5RF United Kingdom Steward Observatory University of Arizona 933 North Cherry Avenue TucsonAZ85721-0065 United States The University of Tokyo Chiba Kashiwa277-8582 Japan ETH Zürich ZürichCH-8093 Switzerland Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38200 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Università di Udine INFN Trieste UdineI-33100 Italy Max-Planck-Institut für Physik MünchenD-80805 Germany Università di Padova INFN PadovaI-35131 Italy Bellaterra BarcelonaE-08193 Spain Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain URCA RJ Rio de Janeiro22290-180 Brazil University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Pola

Recently, several gamma-ray bursts (GRBs) have been detected in the very-high-energy (VHE) gamma-ray energy range by ground-based gamma-ray experiments such as MAGIC, H.E.S.S., and LHASSO. For some GRBs, the VHE emission is consistent with synchrotron self-Compton (SSC) emission from high-energy electrons accelerated in the forward shock of the relativistic jet. However, more statistics are needed to further constrain the emission models. GRB 201216C is a long bright GRB detected in a broad energy range from radio to VHE. The redshift is estimated to be 1.1, making the GRB the most distant source detected in the VHE energy range. MAGIC started the observation 56 seconds after the GRB was triggered by the Swift-BAT telescope. We performed a detailed analysis and detected the signal with about 6 sigma in the first 20 minutes. MAGIC continued the observation for 2.2 hours on the same night and 4 hours on the next night. No signal was detected later than 40 minutes after the GRB trigger. We have performed modelling of the multi-wavelength emission using the MAGIC data. We analysed simultaneous optical data from Liverpool Telescope with MAGIC and included the results in the modelling. The sub-TeV emission is consistent with the single-zone SSC model in the forward shock. In this presentation, we show the final results of the MAGIC data analysis of GRB 201216C and discuss the emission mechanism of the multi-wavelength data. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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AIRR community curation and standardised representation for immunoglobulin and T cell receptor germline sets

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Immunoinformatics (Amsterdam, Netherlands) 2023年 10卷 100025页

作者： William D Lees Scott Christley Ayelet Peres Justin T Kos Brian Corrie Duncan Ralph Felix Breden Lindsay G Cowell Gur Yaari Martin Corcoran Gunilla B Karlsson Hedestam Mats Ohlin Andrew M Collins Corey T Watson Christian E Busse Institute of Structural and Molecular Biology Birkbeck College London England. Human-Centered Computing and Information Science Institute for Systems and Computer Engineering Technology and Science Porto Portugal. Peter O'Donnell Jr. School of Public Health UT Southwestern Medical Center Dallas TX USA. Bioengineering Program Faculty of Engineering Bar-Ilan University Ramat Gan Israel. Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville KY USA. Department of Biological Sciences Simon Fraser University Burnaby BC Canada. Fred Hutchinson Cancer Research Center Seattle WA USA. Peter O'Donnell Jr. School of Public Health Department of Immunology School of Biomedical Sciences UT Southwestern Medical Center Dallas TX USA. Department of Microbiology Tumor and Cell Biology Karolinska Institutet Stockholm Swede. Department of Immunotechnology and SciLifeLab Lund University Lund Sweden. School of Biotechnology and Biomolecular Sciences University of New South Wales Sydney NSW Australia. Division of B Cell Immunology German Cancer Research Center Heidelberg Germany.

Analysis of an individual's immunoglobulin or T cell receptor gene repertoire can provide important insights into immune function. High-quality analysis of adaptive immune receptor repertoire sequencing data depends upon accurate and relatively complete germline sets, but current sets are known to be incomplete. Established processes for the review and systematic naming of receptor germline genes and alleles require specific evidence and data types, but the discovery landscape is rapidly changing. To exploit the potential of emerging data, and to provide the field with improved state-of-the-art germline sets, an intermediate approach is needed that will allow the rapid publication of consolidated sets derived from these emerging sources. These sets must use a consistent naming scheme and allow refinement and consolidation into genes as new information emerges. Name changes should be minimised, but, where changes occur, the naming history of a sequence must be traceable. Here we outline the current issues and opportunities for the curation of germline IG/TR genes and present a forward-looking data model for building out more robust germline sets that can dovetail with current established processes. We describe interoperability standards for germline sets, and an approach to transparency based on principles of findability, accessibility, interoperability, and reusability.

关键词： AIRR-seq Immune receptor Immune receptor germline Immune receptor repertoire Rep-seq

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Lighting consumption optimization using fish school search algorithm

Lighting consumption optimization using fish school search a...

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2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017

作者： Faria, Pedro Pinto, Ângelo Vale, Zita Khorram, Mahsa De Lima Neto, Fernando B. Pinto, Tiago GECAD - Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development ISEP/IPP - Polytechnic of Porto Porto Portugal Polytechnic School of Engineering Computing Engineering Program University of Pernambuco Rua Benfica 455 Recife50720-001 Brazil Calle Espejo 12 Salamanca37007 Spain

ISBN: (纸本)9781538627259

Electricity consumption has increased all around the world in the last decades. This has caused a rise in the use of fossil fuels and in the harming of the environment. In the past years the use of renewable energies and reduction of consumption has growth in order to deal with that problem. The change in the production paradigm led to an increasing search of ways to shorten consumption and adapt to the production. One of the solutions for this problem is to use Demand Response systems. Lighting systems have a major role in electricity consumption, so they are very suitable to be applied in a Demand Response system, optimizing their use. This optimization can be made in different ways being one of them by using a heuristic algorithm. This paper focuses on the use of Fish School Search algorithm to optimize a lighting system, in order to understand its capability of dealing with a problem of this nature and compare it with other algorithms to evaluate its performance. © 2017 IEEE.

关键词： Optimization

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