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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Jiangchang Cheng Yinglong Dai Yao Yuan Hongli Zhu College of Information Science and Engineering Hunan Normal University Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China College of Liberal Arts and Sciences National University of Defense Technology Changsha China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Multimodal data fusion has a long research history since audio-visual speech recognition, which is inspired by the McGurk effect. Because of the limited model capacity of the traditional methods, multimodal data fusion researches are not so popular for a period. Recently, the advances of deep learning techniques open up new opportunities for the multimodal data fusion field. However, there is still a great gap in multimodal data processing ability between artificial intelligence and human beings. Many problems in multimodal data processing are still necessary to be researched. In this work, we propose to gain an insight into the information fusion level and apply different information fusion strategy to different situations. We analyze the different situations of the multimodal data fusion process and divide them into two categories, including consistent information fusion and contradictory information fusion. We demonstrate some toy examples of the different cases of the multimodal data fusion process.

关键词： Deep learning Data privacy Toy manufacturing industry Data integration Speech recognition Security History

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Malay-corpus-enhanced indonesian-chinese neural machine translation 10th

Malay-corpus-enhanced indonesian-chinese neural machine tran...

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10th International Symposium on Intelligence Computation and Applications, ISICA 2018

作者： Liu, Wuying Wang, Lin Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies GuangzhouGuangdong510420 China School of Information Science and Technology Jiujiang University JiujiangJiangxi332005 China Xianda College of Economics and Humanities Shanghai International Studies University Shanghai200083 China

ISBN: (纸本)9789811364723

Due to the lack of structured language resources, low-resource language machine translation often faces difficulties in cross-language semantic paraphrasing. In order to solve the problem of low-resource machine translation from Indonesian to Chinese, a cognate-parallel-corpus-based expanding method of language resources is proposed, and an improved neural machine translation model is trained by the Malay-corpus-enhanced corpus. The improved model can achieve a comparable result as that of Google in the experiment of Indonesian-Chinese machine translation. The experimental results also show that the morphological similarity and semantic equivalence between the languages are very effective computational features to improve the performance of neural machine translation for low-resource languages. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Neural machine translation

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Evaluation of Scholar's Contribution to Team Based on Weighted Co-author Network

Evaluation of Scholar's Contribution to Team Based on Weight...

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2019国际计算机前沿大会

作者： Xinmeng Zhang Xinguang Li Shengyi Jiang Xia Li Bolin Xie Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Non-universal Language Intelligent Processing Laboratory Guangdong University of Foreign Studies School of Information Science and Technology Guangdong University of Foreign Studies

The contributions of scientific researchers include personal influence and talent training *** this paper,using 9964 high-quality coauthor scientific papers in English teaching research from China citation database from 1997 to 2016,a weighted coauthor network with variety factors is constructed.A model was proposed to calculate the author's contribution to the research team by combining personal and network *** results reveal a variety of characteristics of the co-author networks in English teaching research field,including statistical properties,community features,and authors' contribution to teams in this discipline.

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Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework

arXiv

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arXiv 2020年

作者： Pham, Lam Phan, Huy Nguyen, Truc Palaniappan, Ramaswamy Mertins, Alfred McLoughlin, Ian University of Kent School of Computing Medway Kent United Kingdom Signal Processing and Speech Communication Lab Graz University of Technology Austria Institute for Signal Processing University of Lubeck Germany National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei China

This article proposes an encoder-decoder network model for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. We make use of multiple low-level spectrogram features at the front-end, transformed into higher level features through a well-trained CNN-DNN front-end encoder. The high level features and their combination (via a trained feature combiner) are then fed into different decoder models comprising random forest regression, DNNs and a mixture of experts, for back-end classification. We report extensive experiments to evaluate the accuracy of this framework for various ASC datasets, including LITIS Rouen and IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 Task 1, 2017 Task 1, 2018 Tasks 1A & 1B and 2019 Tasks 1A & 1B. The experimental results highlight two main contributions;the first is an effective method for high-level feature extraction from multi-spectrogram input via the novel C-DNN architecture encoder network, and the second is the proposed decoder which enables the framework to achieve competitive results on various datasets. The fact that a single framework is highly competitive for several different challenges is an indicator of its robustness for performing general ASC tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Colligational Patterns in China English:the Case of the Verbs of Communication

Colligational Patterns in China English:the Case of the Verb...

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第十八届中国计算语言学大会暨中国中文信息学会2019学术年会

作者： Lixin Xia Yun Xia Qian Li Laboratory of Language Engineering and Computing/Center for Lexicographical Studies of Guangdong Uni Nanfang College of Sun Yat-Sen University Guangzhou 510970China

ISBN: (纸本)9783030323806

This present study aims to investigate the colligational structures in China English.A corpus-based and comparative methodology was adopted in which three verbs of communication(discuss,communicate and negotiate)were chosen as the research *** Corpus of China English(CCE)was built with 13.9 million *** the colligational patterns of the three verbs were extracted from the CCE by the tool Colligator,and analyzed in comparison with those from the British National Corpus(BNC).The major findings of the study are: 1)China English and British English share some common core colligational patterns,for example,the V *** for the verb discuss,the V *** V *** for the verb communicate,and the V *** V *** for the verb negotiate are the most frequently used patterns of all the colligations in both corpora.2)The number of occurrences of some colligational patterns in CCE is significantly greater than that in *** are V pl.n.,V *** V *** the verb discuss,V *** the verb communicate,and V *** for the verb negotiate.3)Some colligational patterns occur less frequently in CCE than those in BNC,such as the patterns V deter.,V *** V *** for the verb discuss,V *** V *** the verb communicate,and V *** the verb negotiate.4)No new colligational patterns have been found in China *** study extends research on World Englishes,and its findings can attest to the process of structural nativisation of the English language used in the contexts of China.

关键词： Colligation China English language Contact Corpus of China English Computational linguistics Corpus linguistics

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Group Daily Arrival Prediction Based on A Hybrid Model Combing Autoregressive Integrated Moving Average Model and Back Propagation Neural Network

Group Daily Arrival Prediction Based on A Hybrid Model Combi...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Xiaoling Zhu Xi Zhang Ge Shi Yashen Wang National Engineering Laboratory for Risk Perception and Prevention (RPP) China Academy of Electronics and Information Technology of CETC Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Beijing Institute of Technology Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

To solve the problem of predicting daily arrival number of people belonging to a specific group, this paper proposes a hybrid model which integrates linear component (from ARIMA model) with non-linear component (from BP neural network). The experimental results indicate that, hybrid methodology that has both linear and nonlinear modeling capabilities is a good strategy for practical use.

关键词： Predictive models Neurons Data models Analytical models Urban areas Solid modeling Backpropagation

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Robust deep learning framework for predicting respiratory anomalies and diseases

arXiv

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arXiv 2020年

作者： Pham, Lam McLoughlin, Ian Phan, Huy Tran, Minh Nguyen, Truc Palaniappan, Ramaswamy University of Kent School of Computing Medway Kent United Kingdom National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei China Nuffield Department of Anaesthesia University of Oxford United Kingdom Signal Processing and Speech Communication Lab Graz University of Technology Austria

This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model classifies the features into classes of respiratory disease or anomaly. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, evaluate the ability of the framework to classify sounds. Two main contributions are made in this paper. Firstly, we provide an extensive analysis of how factors such as respiratory cycle length, time resolution, and network architecture, affect final prediction accuracy. Secondly, a novel deep learning based framework is proposed for detection of respiratory diseases and shown to perform extremely well compared to state of the art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Effect of students’ investigative experiments on students’ recognition of interference and diffraction patterns: An eye-tracking study

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Physical Review Physics Education Research 2021年第1期17卷 010110-010110页

作者： Ana Susac Maja Planinic Andreja Bubic Katarina Jelicic Lana Ivanjek Karolina Matejak Cvenic Marijan Palmovic Department of Applied Physics Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 10000 Zagreb Croatia Department of Physics Faculty of Science University of Zagreb Bijenicka 32 10000 Zagreb Croatia Chair for Psychology Faculty of Humanities and Social Sciences University of Split Sinjska 2 21000 Split Croatia Physics Education Research Faculty of Physics Technische Universität Dresden Haeckelstraße 3 01069 Dresden Germany Laboratory for Psycholinguistic Research Department of Speech and Language Pathology University of Zagreb Borongajska cesta 83h 10000 Zagreb Croatia

Recognition of interference and diffraction patterns is a difficult task for both high-school and university students. Many students fail to observe important features of particular patterns and identify the differences among similar patterns. In this study, we investigated if performing students’ investigative experiments can help high-school students in recognition of typical interference and diffraction patterns. Students in the experimental group were exposed to a teaching intervention that included five students’ investigative hands-on experiments on wave optics whereas the control group had the standard lecture-based physics teaching. We measured eye movements of students from both the experimental and control groups while they were identifying patterns produced by monochromatic light on a double slit, single slit, and diffraction grating, and by white light on a diffraction grating. Students from the experimental group had a higher percentage of correct answers than students in the control group that indicated that students’ investigative experiments had a positive effect on their recognition of interference and diffraction patterns. However, the low percentage of correct answers, even in the experimental group, confirms that distinguishing of the typical interference and diffraction patterns remains a difficult task for high-school students even if they had performed investigative hands-on experiments. Eye-tracking data showed that students from the experimental group had a shorter dwell on multiple-choice patterns, possibly because they were more familiar with interference and diffraction patterns and felt more confident in choosing the correct pattern. All students attended more to those patterns which they chose as the correct answer and that corroborates the previous findings. Overall, the results indicate that students’ recognition of interference and diffraction patterns can be improved by introducing hands-on investigative experiments in the classroom

关键词： Instructional materials development Instructional strategies Scientific reasoning & problem solving

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Triplet-Center Loss Based Deep Embedding Learning Method for Speaker Verification

Triplet-Center Loss Based Deep Embedding Learning Method for...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Yiheng Jiang Yan Song Jie Yan Lirong Dai Ian McLoughlin National Engineering Laboratory of Speech and Language Information Processing University of Science and Technology of China Hefei China School of Computing University of Kent Medway UK

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

In this work, we introduce an effective loss function, i.e., triplet-center loss, to improve the performance of deep embedding learning methods for speaker verification (SV). The triplet-center loss is combination of triplet loss and center loss so that it shares superiorities of these two loss functions. Comparing with the widely used softmax loss, the main advantage of triplet-center loss is that it learns a center for each class, and it requires distances between samples and centers from the same class are closer than those from different classes. To evaluate the performance of triplet-center loss, we conduct extensive experiments on noisy and unconstrained dataset, i.e., Voxceleb. The results show that triplet-center loss significantly improves the performance of SV. Specifically, it reduces equal error rate (EER) from softmax loss by 11.6%, 10.4% in cosine scoring and PLDA backend, respectively.

关键词： Training Neural networks Task analysis Feature extraction Euclidean distance Learning systems Data mining

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Kidding Bot: A Chatbot against Harassing Phone Calls

Kidding Bot: A Chatbot against Harassing Phone Calls

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作者： Shihong Chen Tianjiao Xu Lu Chen Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies School of Information Science and Technology Guangdong University of Foreign Studies School of Business Guangdong University of Foreign Studies

Nowadays,the majority of mobile phone users suffer from harassment ***,traditional way to intercept the harassment calls cannot reduce this annoying *** this paper,we designed and developed Kidding bot:a chatbot to counterwork the harassment *** the user received the harassment calls,he could connect our Kidding bot server by call forwarding,then Kidding bot starts to talk with the *** bot is combined with Turing chatbot and a dual encoder specially added in sequence to sequence *** anti-harassment telephone test result shows that Kidding bot based our model performed better than the two baselines:a single Turing chatbot and a chatbot based on Sequence-to-Sequence model.

关键词： Kidding bot chatbot harassment phone dual encoder sequence to sequence model

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