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computer science and Data Engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Lomthandazo Matsane Ashwini Jadhav Ritesh Ajoodha School of Computer Science and Applied Mathematics DSI-NICIS National e-Science Postgraduate Teaching and Training Platform (NEPTTP) The University of the Witwatersrand Johannesburg South Africa Faculty of Science The University of the Witwatersrand Johannesburg South Africa School of Computer Science and Applied Mathematics The University of the Witwatersrand Johannesburg South Africa

State-of-the-art Automatic Speech Recognition (ASR) systems convert the spoken words into a corresponding text. One of the problems faced in ASR is that speakers have a different way of pronouncing words, and their accents are different from one speaker to another due to age, gender, nationality, rapidity of words, expressive form of the speaker. This paper uses two data sets, Surrey Audio-Visual Expressed Emotion (SAVEE) and The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) data sets to determine the effect of the tone in the learning environment by using the ASR and check which classifier is giving the best result. Feature such energy, Mel filter Central coefficients, energy etc. were extracted using jAudio and Waikato Environment for Knowledge Analysis (WEKA) data mining tools was used for classification. Classifiers called multilayer Perceptron (MLP) neural network model, Support Vector Machines (SVM), Simple Logistic Regression (SLR), K-Nearest Neighbour (K-NN) and Random Forests (RF) was used to obtain the results of the emotion state for the both data sets. The data sets used to train the classifiers are in ARFF format. The results show that SAVEE data sets overcomes RAVDESS data sets in overall emotion classification performance. The result shows that RF performed better than the other classifier. The performance of classification models is evaluated in WEKA using 10-fold cross validation. The presented study examines seven emotions- anger, happiness, sadness, fear, surprise, disgust and neutral.

关键词： Support vector machines Radio frequency Neural networks Tools Multilayer perceptrons Feature extraction Data mining

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A joint model for dropped pronoun recovery and conversational discourse parsing in Chinese conversational speech

arXiv

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arXiv 2021年

作者： Yang, Jingxuan Xu, Kerui Xu, Jun Li, Si Gao, Sheng Guo, Jun Xue, Nianwen Wen, Ji-Rong School of Artificial Intelligence Beijing University of Posts and Telecommunications China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China Department of Computer Science Brandeis University United States

In this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech. We show that DPR and CDP are closely related, and a joint model benefits both tasks. We refer to our model as DiscProReco, and it first encodes the tokens in each utterance in a conversation with a directed Graph Convolutional Network (GCN). The token states for an utterance are then aggregated to produce a single state for each utterance. The utterance states are then fed into a biaffine classifier to construct a conversational discourse graph. A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer. The joint model is trained and evaluated on a new Structure Parsing-enhanced Dropped Pronoun Recovery (SPDPR) dataset that we annotated with both two types of information. Experimental results on the SPDPR dataset and other benchmarks show that DiscProReco significantly outperforms the state-of-the-art baselines of both tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Recovery

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Topical Issue Scientific Machine Learning (1/2)

引用

GAMM Mitteilungen 2021年第1期44卷

作者： Benner, Peter Klawonn, Axel Stoll, Martin Computational Methods in Systems and Control Theory Max Planck institute for Dynamics of Complex Technical Systems Magdeburg Germany Fakultät für Mathematik Otto-von-Guericke Universität Magdeburg Germany Department of Mathematics and Computer Science University of Cologne Germany Center for Data and Simulation Science University of Cologne Germany Chair of Scientific Computing Department of Mathematics TU Chemnitz Germany

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Hawkes processes on graphons

arXiv

引用

arXiv 2021年

作者： Xu, Hongteng Luo, Dixin Zha, Hongyuan Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Data Science Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

We propose a novel framework for modeling multiple multivariate point processes, each with heterogeneous event types that share an underlying space and obey the same generative mechanism. Focusing on Hawkes processes and their variants that are associated with Granger causality graphs, our model leverages an uncountable event type space and samples the graphs with different sizes from a nonparametric model called graphon. Given those graphs, we can generate the corresponding Hawkes processes and simulate event sequences. Learning this graphon-based Hawkes process model helps to 1) infer the underlying relations shared by different Hawkes processes;and 2) simulate event sequences with different event types but similar dynamics. We learn the proposed model by minimizing the hierarchical optimal transport distance between the generated event sequences and the observed ones, leading to a novel reward-augmented maximum likelihood estimation method. We analyze the properties of our model in-depth and demonstrate its rationality and effectiveness in both theory and experiments. © 2021, CC BY.

关键词： Maximum likelihood estimation

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Construction of practical teaching system for integration of specialism and innovation based on virtual simulation technology 29th

Construction of practical teaching system for integration of...

引用

29th National Conference on computer science Technology and Education, NCCSTE 2019

作者： Wang, Yangping Dang, Jianwu Yong, Jiu Wang, Wenrun Yue, Biao School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China National Computer Science and Technology Experimental Teaching Demonstration Center Lanzhou Jiaotong University Lanzhou China Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou China

ISBN: (纸本)9789811553899

The practical teaching for the majors of rail transit in colleges and universities involves a large number of high-cost, high-consumption, large-scale or comprehensive training. It will often encounter high-risk or extreme environment, inaccessible or irreversible operation, which seriously affects the cultivation of students professional engineering ability. In this paper, a practical teaching system based on virtual simulation technology is constructed for rail transit specialty. The system integrates the professional advantages and practical platform in the university, develops virtual simulation experimental teaching resources and practical teaching cases, and enables students to carry out "independent, cooperative and inquiry" learning in an open, safe, realistic and interactive virtual environment. This system will enhance students professional engineering practice ability and innovation ability, promote the training level of rail transit professionals and the overall quality of students. And for students, it can achieve the goal of strengthening the basic principles of curriculum, training design and research thinking, and enhancing innovation and entrepreneurship ability. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Students

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A general selection method for mutation strategy in differential evolution 10th

A general selection method for mutation strategy in differen...

引用

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

作者： Xia, Dahai Lin, Song Yan, Meng Xiong, Caiquan Li, Yuanxiang Computer School Hubei University of Technology Wuhan China Strategic Teaching and Research Section Naval Command College Nanjing China College of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9789811364723

How to balance exploration and exploitation is a key issue for evolution algorithm including differential algorithm (DE). Many researchers propose various improved mutation strategies to solve this issue for DE. Most of them can be classified as deterministic rules. That is to say, they select individuals according to predetermined methods and so the balance is static. However, different evolution stages require different balance between exploration and exploitation. In order to solve this problem, a general selection method named adaptive stochastic ranking based mutation strategies in DE(ASR-DE). In ASR-DE, it uses stochastic ranking method to rank all individuals according to their contribution in exploration and exploitation. The parameter P f in stochastic ranking is adaptive controlled by a transform version of success rate. The individuals with the smaller ranking are more likely to be selected. 28 functions of CEC2013 is used here to verify the validity of testing method. The test results show that ASR-DE improves the standard DE and improved DE comparing with other methods. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Stochastic systems

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Formation of project competence of future environmentalists

Formation of project competence of future environmentalists

引用

2020 International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters, ICSF 2020

作者： Horbatiuk, Roman Voitovych, Oksana Voitovych, Ihor Volodymyr Hnatyuk Ternopil National Pedagogical University Department of Mechanical Engineering and Transport 2 M. Kryvonosa Str Ternopil46027 Ukraine Rivne State University of Humanities Department of Ecology Geography and Tourism 12 Stepana Bandery Str Rivne33028 Ukraine Rivne State University of Humanities Department of Information and Communication Technologies and Methods of Teaching Computer Science 12 Stepana Bandery Str Rivne33028 Ukraine

We have gained successful experience in conducting educational projects with studentsenvironmentalists of various thematic areas. The topics of ecological projects was related to a practical, professionally oriented block of the curriculum and are intended to deepen the knowledge of individual students in a certain area in order to differentiate the learning process. Most often, project topics relate to a specific practical issue that is relevant to real life. At the same time, it requires the acquisition of students' knowledge from different subjects stimulates systematic creative thinking, turning up the research skills and opens opportunities for effective using of ICT in the project activity. © The Authors, published by EDP sciences, 2020.

关键词： Students

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Leveraging Neural Networks to Profile Health Care Providers with Application to Medicare Claims

arXiv

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arXiv 2023年

作者： Wu, Wenbo Li, Fan Liu, Richard Li, Yiting McAdams-DeMarco, Mara Geras, Krzysztof J. Schaubel, Douglas E. Díaz, Iván Division of Biostatistics Department of Population Health NYU Grossman School of Medicine United States Division of Nephrology Department of Medicine NYU Grossman School of Medicine United States Center for Data Science New York University United States Department of Biostatistics Yale School of Public Health United States Center for Methods in Implementation and Prevention Science Yale School of Public Health United States Department of Surgery NYU Grossman School of Medicine United States Division of Epidemiology Department of Population Health NYU Grossman School of Medicine United States Department of Radiology NYU Grossman School of Medicine United States Department of Computer Science Courant Institute of Mathematical Sciences New York University United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine United States

Encompassing numerous nationwide, statewide, and institutional initiatives in the United States, provider profiling has evolved into a major health care undertaking with ubiquitous applications, profound implications, and high-stakes consequences. In line with such a significant profile, the literature has accumulated a number of developments dedicated to enhancing the statistical paradigm of provider profiling. Tackling wide-ranging profiling issues, these methods typically adjust for risk factors using linear predictors. While this approach is simple, it can be too restrictive to characterize complex and dynamic factor-outcome associations in certain contexts. One such example arises from evaluating dialysis facilities treating Medicare beneficiaries with end-stage renal disease. It is of primary interest to consider how the coronavirus disease (COVID-19) affected 30-day unplanned readmissions in 2020. The impact of COVID-19 on the risk of readmission varied dramatically across pandemic phases. To efficiently capture the variation while profiling facilities, we develop a generalized partially linear model (GPLM) that incorporates a neural network. Considering provider-level clustering, we implement the GPLM as a stratified sampling-based stochastic optimization algorithm that features accelerated convergence. Furthermore, an exact test is designed to identify under- and over-performing facilities, with an accompanying funnel plot to visualize profiles. The advantages of the proposed methods are demonstrated through simulation experiments and profiling dialysis facilities using 2020 Medicare claims from the United States Renal Data System. © 2023, CC BY.

关键词： Optimization

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Who Said What (WSW 2.0)? Enhanced Automated Analysis of Preschool Classroom Speech

arXiv

引用

arXiv 2025年

作者： Sun, Anchen Feng, Tiantian Gutierrez, Gabriela Londono, Juan J. Xu, Anfeng Elbaum, Batya Narayanan, Shrikanth Perry, Lynn K. Messinger, Daniel S. Department of Electrical and Computer Engineering University of Miami Coral GablesFL United States Department of Computer Science Viterbi School of Engineering University of Southern California Los AngelesCA United States Department of Psychology University of Miami Coral GablesFL United States Department of Teaching Learning University of Miami Coral GablesFL United States

This paper introduces an automated framework (WSW 2.0) for analyzing vocal interactions in preschool classrooms, enhancing both accuracy and scalability through the integration of wav2vec2-based speaker classification and Whisper (large-v2 and large-v3) speech transcription. A total of 235 minutes of audio recordings (160 minutes from 12 children and 75 minutes from 5 teachers), were used to compare system outputs to expert human annotations. WSW 2.0 achieves a weighted F1 score of .845, accuracy of .846, and an error-corrected kappa of .672 for speaker classification (child vs. teacher). Transcription quality is moderate to high with word error rates of .119 for teachers and .238 for children. WSW 2.0 exhibits relatively high absolute agreement intraclass correlations (ICC) with expert transcriptions for a range of classroom language features. These include teacher and child mean utterance length, lexical diversity, question asking, and responses to questions and other utterances, which show absolute agreement intraclass correlations between .64 and .98. To establish scalability, we apply the framework to an extensive dataset spanning two years and over 1,592 hours of classroom audio recordings, demonstrating the framework’s robustness for broad real-world applications. These findings highlight the potential of deep learning and natural language processing techniques to revolutionize educational research by providing accurate measures of key features of preschool classroom speech, ultimately guiding more effective intervention strategies and supporting early childhood language development. © 2025, CC BY-NC-ND.

关键词： Natural language processing systems

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Exploring the limits of data augmentation for retinal vessel segmentation

arXiv

引用

arXiv 2021年

作者： Uysal, Enes Sadi Bilici, M. Şafak Zaza, B. Selin Özgenç, M. Yiǧit Boyar, Onur Department of Computer Engineering Yildiz Technical University Istanbul Turkey Department of Industrial Engineering Yildiz Technical University Istanbul Turkey Department of Quantitative Methods Marmara University Istanbul Turkey Department of Computational Science and Engineering Boǧaziçi University Istanbul Turkey

Retinal Vessel Segmentation is important for the diagnosis of various diseases. The research on retinal vessel segmentation focuses mainly on the improvement of the segmentation model which is usually based on U-Net architecture. In our study, we use the U-Net architecture and we rely on heavy data augmentation in order to achieve better performance. The success of the data augmentation relies on successfully addressing the problem of input images. By analyzing input images and performing the augmentation accordingly we show that the performance of the U-Net model can be increased dramatically. Results are reported using the most widely used retina dataset, DRIVE. © 2021, CC BY.

关键词： Diagnosis

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