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Multimodal Lyrics-Rhythm Matching

Multimodal Lyrics-Rhythm Matching

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2022 IEEE international conference on big data, big data 2022

作者： Liao, Callie C. Liao, Duoduo Guessford, Jesse McLean High School United States George Mason University School of Computing Fairfax United States George Mason University School of Music Fairfax United States

ISBN: (纸本)9781665480451

Despite the recent increase in research on artificial intelligence for music, prominent correlations between key components of lyrics and rhythm such as keywords, stressed syllables, and strong beats are not frequently studied. This is likely due to challenges such as audio misalignment, inaccuracies in syllabic identification, and most importantly, the need for cross-disciplinary knowledge. To address this lack of research, we propose a novel multimodal lyrics-rhythm matching approach in this paper that specifically matches key components of lyrics and music with each other without any language limitations. We use audio instead of sheet music with readily available metadata, which creates more challenges yet increases the application flexibility of our method. Furthermore, our approach creatively generates several patterns involving various multimodalities, including music strong beats, lyrical syllables, auditory changes in a singer's pronunciation, and especially lyrical keywords, which are utilized for matching key lyrical elements with key rhythmic elements. This advantageous approach not only provides a unique way to study auditory lyrics-rhythm correlations including efficient rhythm-based audio alignment algorithms, but also bridges computational linguistics with music as well as music cognition. Our experimental results reveal an 0.81 probability of matching on average, and around 30% of the songs have a probability of 0.9 or higher of keywords landing on strong beats, including 12% of the songs with a perfect landing. Also, the similarity metrics are used to evaluate the correlation between lyrics and rhythm. It shows that nearly 50% of the songs have 0.70 similarity or higher. In conclusion, our approach contributes significantly to the lyrics-rhythm relationship by computationally unveiling insightful correlations. © 2022 IEEE.

关键词： Music

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Convolutional Transformer with Similarity-based Boundary Prediction for Action Segmentation 34

Convolutional Transformer with Similarity-based Boundary Pre...

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34th IEEE international conference on Tools with Artificial intelligence (ICTAI)

作者： Du, Dazhao Su, Bing Li, Yu Qi, Zhongang Si, Lingyu Shan, Ying Chinese Acad Inst Software Beijing Peoples R China Univ Chinese Acad Beijing Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Int Digital Econ Acad Beijing Peoples R China ARC Lab Tencent PCG Beijing Peoples R China

ISBN: (纸本)9798350397444

Action classification has made great progress, but segmenting and recognizing actions from long videos remains a challenging problem. Recently, Transformer-based models with strong sequence modeling ability have succeeded in many sequence modeling tasks. However, the lack of inductive bias and the difficulty of handling long video sequences limit the application of the Transformer in the action segmentation task. In order to explore the potential of the Transformer in this task, we replace some specific linear layers in the vanilla Transformer with dilated temporal convolution, and a sparse attention mechanism is utilized to reduce the time and space complexities to process long video sequences. Besides, directly using frame-wise classification loss to train the model will cause that frames at boundaries of actions are treated equally with those in the middle of actions, and the learned features are not sensitive to boundaries. We propose a new local log-context attention module to predict whether each frame is at the beginning, middle, or end of an action. Since boundary frames are similar to their neighboring frames of different classes, our similarity-based boundary prediction helps learn more discriminative features. Extensive experiments on three datasets show the effectiveness of our method.

关键词： Computer Vision Video Action Segmentation Transfromer Temporal Convolutional Neural Network

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Anomaly Detection Method of Healthcare Internet of Things Gateway Supporting Edge computing 3rd

Anomaly Detection Method of Healthcare Internet of Things Ga...

引用

3rd EAI international conference on IoT and big data Technologies for Health Care, IotCARE 2022

作者： Zou, Zixiu Hu, Yi Liu, Xinyao Zhuo, Shufeng Fuzhou Institute of Technology Fujian Fuzhou350506 China SEGI University Selangor Petaling Jaya47810 Malaysia Henan Information Engineering School Zhengzhou450000 China Imperial Vision Technology Company Limited Fuzhou350001 China The Internet of Things and Artificial Intelligence College Fujian Polytechnic of Information Technology Fuzhou350003 China

ISBN: (纸本)9783031335440

As the link between the perception layer and the network layer, the Internet of Things gateway is of great significance to the safe and stable operation of the healthcare Internet of Things. Once the gateway is abnormal, it will directly affect the information transmission in health care work. Therefore, an anomaly detection method for the gateway of the Internet of Things in health care supporting edge computing is proposed. Several representative gateway status indicators are selected by using the maximum uncorrelation method, and the gateway anomaly detection task is unloaded to the edge server by using edge computing. An anomaly detection model based on SOFM neural network and random forest is constructed to realize the anomaly detection of the Internet of Things gateway in health care. The experimental results show that the determination coefficients of the six types of samples of this method are more than 0.9, which is close to 1, which shows that this method has better anomaly detection performance of the Internet of Things gateway in health care. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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A Method for Calculating the Potential Crowdedness of Urban Emergency Shelters 23

A Method for Calculating the Potential Crowdedness of Urban ...

引用

23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Xinxiang Guan, Minglei Hu, Junjie Tian, Chenyang Shi, Hong Li, Qingquan School of Marine Technology and Geomatics Jiangsu Ocean University Jiangsu China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China Shenzhen University Guangdong Key Laboratory of Urban Informatics Shenzhen Shenzhen China

ISBN: (纸本)9781665494571

The evaluation of the suitability of urban emergency shelters is very important, and mostly carried out at three indicators: effectiveness, accessibility, and safety. The potential crowdedness of emergency shelters is also an important indicator for evaluating the suitability of urban emergency shelters. It can help relevant departments to reasonably allocate emergency resources and plan and build subsequent emergency shelters. In this paper, we improve the calculation method of the potential crowdedness of emergency shelters from three aspects: Distribution of the Refuge Population, the attractiveness of shelters, and accessibility of evacuation places. The influence of factors such as population size, shelter service capacity, shelter service radius, number of command and evacuation agencies, and the distance decay effect on the potential number of people served by the shelter is taken into account. And quantitatively analyzed the potential crowdedness of each emergency shelter within it, using downtown Shanghai as an example. The results of the study show that nearly two-thirds of the emergency shelters in downtown Shanghai exceed the minimum effective area per capita specified in the code and that the service is highly congested, especially in the three districts of Jing'an, Yangpu, and Putuo, which are the most serious, and need to be appropriately increased to alleviate the service pressure. And there is a serious shortage of emergency places on the east side of Downtown Shanghai to meet the shelter needs of residents. The research methodology used in this paper can reasonably reflect the potential crowdedness of emergency shelters, and provide a reference for the authorities concerned in allocating emergency resources and planning the construction of emergency shelters. © 2021 IEEE.

关键词： Graphical models Codes Smart cities Sociology Area measurement Planning Safety

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Admission Probability Prediction of College Entrance Examination Based on Siamese Network 6

Admission Probability Prediction of College Entrance Examina...

引用

6th international conference on Electronic Information Technology and Computer Engineering, EITCE 2022

作者： Gao, Sida Liu, Tianming Fu, Lijun Chen, Xueliang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Laboratory of Big Data and Artificial Intelligence Technology Shandong University Beijing China

ISBN: (纸本)9781450397148

For students taking the College Entrance Examination, the choice of major is as important as their scores. The choice is often related to whether the candidate can be admitted with a higher probability. Therefore, how to accurately predict the admission probability has become the important problem. In this paper, by using the candidate's provincial rank in the current year and the provincial enrollment rank of each major in the past three years, Siamese network structure combined with gated recurrent unit is used to predict the admission probability of the candidate in each major. It was found that the model can accurately calculate the admission probability, and can capture the time series information of the rank change in previous years. Compared with the traditional average method, it has a great improvement. © 2022 Association for computing Machinery.

关键词： Forecasting

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Real-Time big data Analysis Using Web Scraping in Apache Spark Environment: Case Study—Mobile data Analysis from Flipkart 3rd

Real-Time Big Data Analysis Using Web Scraping in Apache Spa...

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3rd international conference on Recent Trends in Advanced computing - Artificial intelligence and Technologies, ICRTAC-AIT 2020

作者： Ganguly, Pushpita Parihar, Giriraj Sivagami, M. School of Computer Science and Engineering VIT University Tamil Nadu Chennai India

ISBN: (纸本)9789811664472

There are many data sources that produce sheer volumes of data, and this generated data named big data can be used for making decisions. There are lots of domains including finance, transportation, entertainment, energy, security, and emergency services rely on fast and efficient analytics based on available data to make quality decisions conveniently which is a key factor for businesses and many service industries. The big data nature requires new distributed processing approaches to extract the valuable information. Real-time sentiment analysis is one of the most demanding research areas that require powerful big data analytics tools such as Spark. This paper proposed a real-time data analysis. It first scraps data from the Web and will do real-time analysis using Spark with machine learning tools. As a case study for this real-time analysis, Flipkart mobile data is taken with the aim of doing classification of the dataset for further analysis as well as predicting the ratings of different kinds of mobile phones using machine learning modules. At last the accuracy of the implemented models is shown. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： big data

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Research on the Application and Analysis of Mechanical Equipment intelligence Based on Knowledge Graph 2

Research on the Application and Analysis of Mechanical Equip...

引用

2nd international conference on Computer, big data and Artificial intelligence, ICCBDAI 2021

作者： Qi, Rui Chen, Jing Yu, ZengWen Ke, WenJun Wang, Rui Graduate School of the Second Research Institute of China Aerospace Science and Industry Corporation Beijing China Beijing Institute of Computer Technology and Applications Beijing China Xidian University Shaanxi Xi'an China Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

With the rapid evolution of manufacturing technology, mechanical equipment products are being replaced more frequently, and competition in the industry is becoming increasingly fierce. The collection and analysis of mechanical equipment technical intelligence is a key need for many enterprises. Using artificial intelligence technology to build an intelligence analysis system for all types of data is the inevitable trend of intelligence analysis. This paper proposes an intelligent processing and analysis framework of mechanical equipment intelligence. Based on the construction of knowledge graph, the knowledge embedding model is used to complete knowledge reasoning, predict development trends for mechanical equipment, and allow for more intelligent decision-making. A case study involving 3D printing shows the functions of the intelligence process and analysis framework. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： 3D printers

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Developing Songjeong Metaverse Surfing Village: Development of Metaverse-based Platform Specialized for Marine Tourism 7

Developing Songjeong Metaverse Surfing Village: Development ...

引用

7th IEEE/ACIS international conference on big data, Cloud computing, and data Science, BCD 2022

作者： Yoon, Tae Hwan Do, Jong Kyu Jeong, Seok Chan Dong-Eui University Dept. of Hotel & Convention Management Busan Korea Republic of Dong-Eui University Graduate School of Engineering Dept. of Artificial Intelligence Busan Korea Republic of Dong-Eui University College of Commerce and Economics & AI Grand ICT Research Center Dept. of E-Business Busan Korea Republic of

ISBN: (数字)9781665465823

ISBN: (纸本)9781665465823

This study aims to introduce and explain how to develop a metaverse based immersive and experiential tourist contents. The project plans to introduce long-term and highly scalable new concept platform by developing virtual metaverse surfing village centered on Song-Jeong beach which is popular surfing destination in South Korea. The project consists of the following 5 tasks: The first Task is to develop Metaverse-based hyper-immersive interactive content which include implementation of surfing village virtualization by identifying an analyzing the components through spatial analysis. Second Task is developing VR/AR based experiential content. In this task, AI based service contents that can provide optimized surfing posture and correction service and virtual surfing experience contents will be developed. The third task is developing contents that provide integrated information linked to local tourism and leisure infrastructure. To do that AI based smart tourism service will be developed. The fourth task is to develop business model for interactive metaverse platform. Lastly, the fifth task is demonstration stage of the platform. This task includes promotion of demonstration platform, operating expert advisory group, and establishment of longterm expansion plan. © 2022 IEEE.

关键词： Tourism

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Research on data analysis of Internet consumer finance and consumer behavior based on VAR model

Research on data analysis of Internet consumer finance and c...

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2022 international conference on Cyber Security, Artificial intelligence, and Digital Economy, CSAIDE 2022

作者： Liu, Lei Meng, Lei School of Finance Harbin University of Commerce China

ISBN: (纸本)9781510657304

With the rapid development of digital information technologies such as big data, cloud computing and artificial intelligence, quantum information technology and digital economy are profoundly changing our way of production and life. New forms of digital economy, such as industrial Internet, intelligent Internet of things and online shopping, have become new driving forces for high-quality economic development. As a branch of digital economy, Internet consumer finance is in the trend of continuous development. This paper mainly studies the influence of Internet consumer finance on residents' consumption through VAR model. VAR model constructs the model by combining every endogenous variable in the system with its hysteresis value and exogenous variable, which has a good prediction effect on interrelated time series variables. In addition, the dynamic impact of different types of random error terms on system variables can also be analyzed. Therefore, VAR model is used in this paper to explore the benign interaction between the development of Internet consumer finance and consumer consumption. Through a series of empirical tests and analyses such as ADF test, VAR parameter estimation and impulse response function, the linear relationship between Internet consumer finance and consumption level is quantified and the future development is predicted. A series of countermeasures and suggestions are put forward for reference by relevant researchers. © 2022 SPIE.

关键词： Impulse response

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Design of Intelligent Autonomous Learning Management System Based on Artificial intelligence Technology

Design of Intelligent Autonomous Learning Management System ...

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2022 international conference on Education, Network and Information Technology, ICENIT 2022

作者： Cheng, Juanjuan China West Normal University Sichuan Province Nanchong China

ISBN: (纸本)9781665463072

In recent years, with the rapid development of the Internet, artificial intelligence technology is constantly leading the reform and development of traditional education. At the same time, the concept of guiding students to learn independently and advocating diversified teaching methods is becoming more and more mature. How to realize the design of intelligent autonomous learning management system is a major problem to be solved urgently in the development of autonomous learning. Based on the related applications of artificial intelligence technology, this paper researches and designs the intelligent autonomous learning management system to meet the needs of the current education field for the intelligent autonomous learning management system. This paper takes the development of modern Internet with big data, cloud computing and artificial intelligence as the main characteristics, and builds a complete intelligent autonomous learning management system, which can give full play to the subjectivity of students, help students to strengthen their learning motivation, formulate learning plans;strengthen learning initiative, Explore learning methods;stabilize one's own emotions and relieve learning pressure. The final results of the study show that the average learning attitude of the new class using the intelligent autonomous learning management system is 45.8, the average learning responsibility is 46.1, the average planning arrangement is 47.4, the average monitoring reflection is 45.5, and the average management resource is 46.9. The new class using the intelligent autonomous learning management system is superior to the traditional class in five dimensions: learning attitude, learning responsibility, planning, monitoring and reflection, and management resources. © 2022 IEEE.

关键词： Students

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