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Machine Learning, IOT and Blockchain Technologies & Trends最新文献

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Music Signal Analysis: Regression Analysis 音乐信号分析:回归分析
Machine Learning, IOT and Blockchain Technologies & Trends Pub Date : 1900-01-01 DOI: 10.5121/csit.2021.111205
V. Chivukula, Sri Keshava Reddy Adupala
{"title":"Music Signal Analysis: Regression Analysis","authors":"V. Chivukula, Sri Keshava Reddy Adupala","doi":"10.5121/csit.2021.111205","DOIUrl":"https://doi.org/10.5121/csit.2021.111205","url":null,"abstract":"Machine learning techniques have become a vital part of every ongoing research in technical areas. In recent times the world has witnessed many beautiful applications of machine learning in a practical sense which amaze us in every aspect. This paper is all about whether we should always rely on deep learning techniques or is it really possible to overcome the performance of simple deep learning algorithms by simple statistical machine learning algorithms by understanding the application and processing the data so that it can help in increasing the performance of the algorithm by a notable amount. The paper mentions the importance of data pre-processing than that of the selection of the algorithm. It discusses the functions involving trigonometric, logarithmic, and exponential terms and also talks about functions that are purely trigonometric. Finally, we discuss regression analysis on music signals.","PeriodicalId":347682,"journal":{"name":"Machine Learning, IOT and Blockchain Technologies & Trends","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122501283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Summarization of Commercial Contracts 商务合同摘要
Machine Learning, IOT and Blockchain Technologies & Trends Pub Date : 1900-01-01 DOI: 10.5121/csit.2021.111202
K. Balachandar, Anam Saatvik Reddy, A. Shahina, Nayeemulla Khan
{"title":"Summarization of Commercial Contracts","authors":"K. Balachandar, Anam Saatvik Reddy, A. Shahina, Nayeemulla Khan","doi":"10.5121/csit.2021.111202","DOIUrl":"https://doi.org/10.5121/csit.2021.111202","url":null,"abstract":"In this paper, we propose a novel system for providing summaries for commercial contracts such as Non- Disclosure Agreements (NDAs), employment agreements, etc. to enable those reviewing the contract to spend less time on such reviews and improve understanding as well. Since it is observed that a majority of such commercial documents are paragraphed and contain headings/topics followed by their respective content along with their context, we extract those topics and summarize them as per the user’s need. In this paper, we propose that summarizing such paragraphs/topics as per requirements is a more viable approach than summarizing the whole document. We use extractive summarization approaches for this task and compare their performance with human-written summaries. We conclude that the results of extractive techniques are satisfactory and could be improved with a large corpus of data and supervised abstractive summarization methods.","PeriodicalId":347682,"journal":{"name":"Machine Learning, IOT and Blockchain Technologies & Trends","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130807017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
A Self-Aggregated Hierarchical Topic Model for Short Texts 短文本的自聚合层次主题模型
Machine Learning, IOT and Blockchain Technologies & Trends Pub Date : 1900-01-01 DOI: 10.5121/csit.2021.111212
Yue Niu, Hongjie Zhang
{"title":"A Self-Aggregated Hierarchical Topic Model for Short Texts","authors":"Yue Niu, Hongjie Zhang","doi":"10.5121/csit.2021.111212","DOIUrl":"https://doi.org/10.5121/csit.2021.111212","url":null,"abstract":"With the growth of the internet, short texts such as tweets from Twitter, news titles from the RSS, or comments from Amazon have become very prevalent. Many tasks need to retrieve information hidden from the content of short texts. So ontology learning methods are proposed for retrieving structured information. Topic hierarchy is a typical ontology that consists of concepts and taxonomy relations between concepts. Current hierarchical topic models are not specially designed for short texts. These methods use word co-occurrence to construct concepts and general-special word relations to construct taxonomy topics. But in short texts, word cooccurrence is sparse and lacking general-special word relations. To overcome this two problems and provide an interpretable result, we designed a hierarchical topic model which aggregates short texts into long documents and constructing topics and relations. Because long documents add additional semantic information, our model can avoid the sparsity of word cooccurrence. In experiments, we measured the quality of concepts by topic coherence metric on four real-world short texts corpus. The result showed that our topic hierarchy is more interpretable than other methods.","PeriodicalId":347682,"journal":{"name":"Machine Learning, IOT and Blockchain Technologies & Trends","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114177351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Lyrics to Music Generator: Statistical Approach 歌词音乐生成器:统计方法
Machine Learning, IOT and Blockchain Technologies & Trends Pub Date : 1900-01-01 DOI: 10.5121/csit.2021.111209
Aditya Chivukula, Abhiram Reddy Cholleti, R. Balabantaray
{"title":"Lyrics to Music Generator: Statistical Approach","authors":"Aditya Chivukula, Abhiram Reddy Cholleti, R. Balabantaray","doi":"10.5121/csit.2021.111209","DOIUrl":"https://doi.org/10.5121/csit.2021.111209","url":null,"abstract":"Natural Language Processing is in growing demand with recent developments. This Generator model is one such example of a music generation system conditioned on lyrics. The model proposed has been tested on songs having lyrics written only in English, but the idea can be generalized to various languages. This paper’s objective is to mainly explain how one can create a music generator using statistical machine learning methods. This paper also explains how effectively outputs can be formulated, which are the music signals as they are million sized over a short period frame. The parameters mentioned in the paper only serve an explanatory purpose. This paper discusses the effective statistical formulation of output thereby decreasing the vast amount of estimation of output parameters, and how to reconstruct the audio signals from predicted parameters by using ‘phase-shift algorithm’.","PeriodicalId":347682,"journal":{"name":"Machine Learning, IOT and Blockchain Technologies & Trends","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126880584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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