Tian Xia, Qibo Sun, Ao Zhou, Shangguang Wang, Shilong Xiong, Siyi Gao, Jinglin Li, Quan Yuan
Predicting stock trend by using machining learning is a hot research issue today. However, due to the non linearity and instability of the stock data, it is still very difficult to predict the stock trend with high accuracy. In order to improve the accuracy, most researchers focus on the models selection and features construction. A variety of feature construction methods have been proposed. However, not all features constructed in those paper are equally useful. Further more, many features of significant importance may not be selected in prediction. In order to improve the accuracy of stock trend prediction, this paper will focus on the features selection problem. Most feature selection methods employed in the stock trend prediction are based on filtration methods. Wrapper methods are rarely used. Compared with filtration methods, wrapper methods have better stability and accuracy. In this paper, we propose a feature selection algorithm by extending genetic algorithm (GA). Experiments are conducted on real-world stock price data set. The experiment results show that our GA-based feature selection algorithm is better in both stability and performance.
{"title":"Improving the Performance of Stock Trend Prediction by Applying GA to Feature Selection","authors":"Tian Xia, Qibo Sun, Ao Zhou, Shangguang Wang, Shilong Xiong, Siyi Gao, Jinglin Li, Quan Yuan","doi":"10.1109/SC2.2018.00025","DOIUrl":"https://doi.org/10.1109/SC2.2018.00025","url":null,"abstract":"Predicting stock trend by using machining learning is a hot research issue today. However, due to the non linearity and instability of the stock data, it is still very difficult to predict the stock trend with high accuracy. In order to improve the accuracy, most researchers focus on the models selection and features construction. A variety of feature construction methods have been proposed. However, not all features constructed in those paper are equally useful. Further more, many features of significant importance may not be selected in prediction. In order to improve the accuracy of stock trend prediction, this paper will focus on the features selection problem. Most feature selection methods employed in the stock trend prediction are based on filtration methods. Wrapper methods are rarely used. Compared with filtration methods, wrapper methods have better stability and accuracy. In this paper, we propose a feature selection algorithm by extending genetic algorithm (GA). Experiments are conducted on real-world stock price data set. The experiment results show that our GA-based feature selection algorithm is better in both stability and performance.","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115215719","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}
Twitter, a micro-blogging service, has been generating a large amount of data every minute as it gives people chance to express their thoughts and feelings quickly and clearly about any topics. To obtain the desired information from these available big data, it requires high-performance parallel computing tools along with machine learning algorithms' support. Emerging big data processing frameworks (e.g. Hadoop) can handle such big data effectively. In this paper, we, firstly introduce a novel approach to automatically classify Twitter data obtained from British Geological Survey (BGS), collected using some specific keywords such as landslide, landslides, mudslide, landfall, landslip, soil sliding, based on tweet post date and the countries where tweets are posted using MapReduce algorithm. We then propose a model to distinguish the tweets if they are landslides-related using Naïve-Bayes machine learning algorithm with n-Grams language model on Mahout. This paper also describes an algorithm for the pre-processing steps to make the semi-structured Twitter text data ready for classification. The proposed methods are useful for the BGS and other interested people to be able to see the name and number of the countries where the tweets are sent, the number of tweets sent from each country, the dates and time intervals of the tweets, and to classify the tweets whether they are related to landslides.
{"title":"Social Media Data Analysis Using MapReduce Programming Model and Training a Tweet Classifier Using Apache Mahout","authors":"Umit Demirbaga, D. Jha","doi":"10.1109/SC2.2018.00024","DOIUrl":"https://doi.org/10.1109/SC2.2018.00024","url":null,"abstract":"Twitter, a micro-blogging service, has been generating a large amount of data every minute as it gives people chance to express their thoughts and feelings quickly and clearly about any topics. To obtain the desired information from these available big data, it requires high-performance parallel computing tools along with machine learning algorithms' support. Emerging big data processing frameworks (e.g. Hadoop) can handle such big data effectively. In this paper, we, firstly introduce a novel approach to automatically classify Twitter data obtained from British Geological Survey (BGS), collected using some specific keywords such as landslide, landslides, mudslide, landfall, landslip, soil sliding, based on tweet post date and the countries where tweets are posted using MapReduce algorithm. We then propose a model to distinguish the tweets if they are landslides-related using Naïve-Bayes machine learning algorithm with n-Grams language model on Mahout. This paper also describes an algorithm for the pre-processing steps to make the semi-structured Twitter text data ready for classification. The proposed methods are useful for the BGS and other interested people to be able to see the name and number of the countries where the tweets are sent, the number of tweets sent from each country, the dates and time intervals of the tweets, and to classify the tweets whether they are related to landslides.","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131093943","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}
{"title":"SC2 2018 Program Committee","authors":"","doi":"10.1109/sc2.2018.00007","DOIUrl":"https://doi.org/10.1109/sc2.2018.00007","url":null,"abstract":"","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116699844","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}
With the development of collaborative cloud storage services, files have been typically stored and secured through encryption making them hard to retrieve and search. Search over encrypted cloud approaches have consequently been utilizing cryptographic and indexing procedures. The vast majority use exact matching to fulfill their search criteria, which is then expanded by incorporating similarity ranking algorithms. However, this complex expansion does not always succeed due to its dependence on third parties to evaluate the search and the possible compromise on the privacy of the stored information. It also requires significant computational resources. This work demonstrates novel approach to similarity search, known as Contextual Oblivious Similarity based Search (COS2). In the proposed system, authorized users can categories searches resilient to typing errors. COS2 also introduces browsing caches to improve subscriber experience. Dual encryption mechanisms improve the relevance in searches without revealing confidential data on untrusted cloud service providers. Finally, this contextual search thrives to reduce the computational overhead of the overall search procedure, leading to a 86% improvement in terms of search efficiency.
{"title":"Contextual Oblivious Similarity Searching for Encrypted Data on Cloud Storage Services","authors":"Sneha Umesh Lavnis, D. Elango, H. González-Vélez","doi":"10.1109/SC2.2018.00017","DOIUrl":"https://doi.org/10.1109/SC2.2018.00017","url":null,"abstract":"With the development of collaborative cloud storage services, files have been typically stored and secured through encryption making them hard to retrieve and search. Search over encrypted cloud approaches have consequently been utilizing cryptographic and indexing procedures. The vast majority use exact matching to fulfill their search criteria, which is then expanded by incorporating similarity ranking algorithms. However, this complex expansion does not always succeed due to its dependence on third parties to evaluate the search and the possible compromise on the privacy of the stored information. It also requires significant computational resources. This work demonstrates novel approach to similarity search, known as Contextual Oblivious Similarity based Search (COS2). In the proposed system, authorized users can categories searches resilient to typing errors. COS2 also introduces browsing caches to improve subscriber experience. Dual encryption mechanisms improve the relevance in searches without revealing confidential data on untrusted cloud service providers. Finally, this contextual search thrives to reduce the computational overhead of the overall search procedure, leading to a 86% improvement in terms of search efficiency.","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117251043","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}
Carlos Molina-Jiménez, Ioannis Sfyrakis, E. Solaiman, Irene Ng, M. Wong, A. Chun, J. Crowcroft
Decentralised (on-blockchain) and centralised (off–blockchain) platforms are available for the implementation of smart contracts. However, none of the two alternatives can individually provide the services and quality of services (QoS) imposed on smart contracts involved in a large class of applications. The reason is that blockchain platforms suffer from scalability, performance, transaction costs and other limitations. Likewise, off–blockchain platforms are afflicted by drawbacks emerging from their dependence on single trusted third parties. We argue that in several applications, hybrid platforms composed from the integration of on and off–blockchain platforms are more adequate. Developers that informatively choose between the three alternatives are likely to implement smart contracts that deliver the expected QoS. Hybrid architectures are largely unexplored. To help cover the gap and as a proof of concept, in this paper we discuss the implementation of smart contracts on hybrid architectures. We show how a smart contract can be split and executed partially on an off–blockchain contract compliance checker and partially on the rinkeby ethereum network. To test the solution, we expose it to sequences of contractual operations generated mechanically by a contract validator tool.
{"title":"Implementation of Smart Contracts Using Hybrid Architectures with On and Off–Blockchain Components","authors":"Carlos Molina-Jiménez, Ioannis Sfyrakis, E. Solaiman, Irene Ng, M. Wong, A. Chun, J. Crowcroft","doi":"10.1109/SC2.2018.00018","DOIUrl":"https://doi.org/10.1109/SC2.2018.00018","url":null,"abstract":"Decentralised (on-blockchain) and centralised (off–blockchain) platforms are available for the implementation of smart contracts. However, none of the two alternatives can individually provide the services and quality of services (QoS) imposed on smart contracts involved in a large class of applications. The reason is that blockchain platforms suffer from scalability, performance, transaction costs and other limitations. Likewise, off–blockchain platforms are afflicted by drawbacks emerging from their dependence on single trusted third parties. We argue that in several applications, hybrid platforms composed from the integration of on and off–blockchain platforms are more adequate. Developers that informatively choose between the three alternatives are likely to implement smart contracts that deliver the expected QoS. Hybrid architectures are largely unexplored. To help cover the gap and as a proof of concept, in this paper we discuss the implementation of smart contracts on hybrid architectures. We show how a smart contract can be split and executed partially on an off–blockchain contract compliance checker and partially on the rinkeby ethereum network. To test the solution, we expose it to sequences of contractual operations generated mechanically by a contract validator tool.","PeriodicalId":340244,"journal":{"name":"2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2)","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115006766","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}
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