citations/Readme.md

@article{Smith,
author={Smith, Mark and Doe, John},
title={Article Title: A Good Academic Article Title},
journal={Research in Researching Techniques},
year={2001},
volume={3},
number={1},
pages={226--243},
note={\url{https://doi.org/10.5915/A.DOI.LINK}}
}

@inproceedings{Doe,
 author = {Doe, John and Smith, John},
 title = {Great Paper Title: You Can Tell from the Colon Use},
 booktitle = {Proceedings of the 2015 International Conference on Proceedings.},
 year = {2015},
 pages = {919--927},
 numpages = {9},
 note = {\url{http://dl.acm.org/citation.cfm?id=xxxxxx.xxx}},
 publisher = {City: Publisher Name}
} 

@book{Booker,
  author = {Booker, John},
  publisher = {City: City University Press},
  title = {Book Title},
  year = {2016}
}


@misc{WhitePaper,
  title={Whitepaper On Blockchain Idea},
  author={Chain, Block},
  howpublished={(accessed XX October 20XX) \url{https://realwebsite.com/realwhitepaper.pdf}},
  pages={19},
  year={2016}
}


@Misc{note1,
 note ={Details available at \url{https://ledger.pitt.edu/ojs/index.php/ledger/about/submissions#authorGuidelines/}},
}


@Misc{Nakamoto2008,
  author = {Nakamoto, Satoshi},
  title  = {Bitcoin: A Peer-to-Peer Electronic Cash System},
  year   = {2008},
  howpublished = {(accessed XX October 20XX) \url{https://bitcoin.org/bitcoin.pdf}}
}

@ARTICLE{dnn,
  author={Sze, Vivienne and Chen, Yu-Hsin and Yang, Tien-Ju and Emer, Joel S.},
  journal={Proceedings of the IEEE}, 
  title={Efficient Processing of Deep Neural Networks: A Tutorial and Survey}, 
  year={2017},
  volume={105},
  number={12},
  pages={2295-2329},
  doi={10.1109/JPROC.2017.2761740}}
@inproceedings{chen2016xgboost,
  title={Xgboost: A scalable tree boosting system},
  author={Chen, Tianqi and Guestrin, Carlos},
  booktitle={Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining},
  pages={785--794},
  year={2016}
}
@article{hochreiter1997long,
  title={Long short-term memory},
  author={Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  journal={Neural computation},
  volume={9},
  number={8},
  pages={1735--1780},
  year={1997},
  publisher={MIT press}
}
@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}


@article{araci2019finbert,
  title={Finbert: Financial sentiment analysis with pre-trained language models},
  author={Araci, Dogu},
  journal={arXiv preprint arXiv:1908.10063},
  year={2019}
}

%Here is our citation
@article{doi:10.1080/20479700.2020.1843887,
author = {Mohsen Attaran},
title = {Blockchain technology in healthcare: Challenges and opportunities},
journal = {International Journal of Healthcare Management},
volume = {15},
number = {1},
pages = {70-83},
year = {2022},
publisher = {Taylor & Francis},
doi = {10.1080/20479700.2020.1843887},
URL = {https://doi.org/10.1080/20479700.2020.1843887
},
eprint = {https://doi.org/10.1080/20479700.2020.1843887
}
}
%2
@article{10.1093/jamia/ocx068,
    author = {Kuo, Tsung-Ting and Kim, Hyeon-Eui and Ohno-Machado, Lucila},
    title = "{Blockchain distributed ledger technologies for biomedical and health care applications}",
    journal = {Journal of the American Medical Informatics Association},
    volume = {24},
    number = {6},
    pages = {1211-1220},
    year = {2017},
    month = {09},
    doi = {10.1093/jamia/ocx068},
    url = {https://doi.org/10.1093/jamia/ocx068},
    eprint = {https://academic.oup.com/jamia/article-pdf/24/6/1211/34149246/ocx068.pdf},
}


%3
@inproceedings{10.1145/3311350.3347178,
author = {Scholten, Oliver James and Hughes, Nathan Gerard Jayy and Deterding, Sebastian and Drachen, Anders and Walker, James Alfred and Zendle, David},
title = {Ethereum Crypto-Games: Mechanics, Prevalence, and Gambling Similarities},
year = {2019},
isbn = {9781450366885},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi-org.proxy.lib.duke.edu/10.1145/3311350.3347178},
doi = {10.1145/3311350.3347178},
booktitle = {Proceedings of the Annual Symposium on Computer-Human Interaction in Play},
pages = {379–389},
numpages = {11},
keywords = {regulation, gambling, ethereum, distributed ledger, crypto-games, blockchain gaming},
location = {, Barcelona, Spain, },
series = {CHI PLAY '19}
}
%4
@article {UnderstandingthemechanicsandconsumerrisksassociatedwithplaytoearnP2Egaming,
      author = "Paul Delfabbro and Amelia Delic and Daniel L. King",
      title = "Understanding the mechanics and consumer risks associated with play-to-earn (P2E) gaming",
      journal = "Journal of Behavioral Addictions",
      year = "2022",
      publisher = "Akadémiai Kiadó",
      address = "Budapest, Hungary",
      volume = "11",
      number = "3",
      doi = "10.1556/2006.2022.00066",
      pages=      "716 - 726",
      url = "https://akjournals.com/view/journals/2006/11/3/article-p716.xml"
}

%5
@article{tapscott2017blockchain,
  title={How blockchain is changing finance},
  author={Tapscott, Alex and Tapscott, Don},
  journal={Harvard Business Review},
  volume={1},
  number={9},
  pages={2--5},
  year={2017}
}
%6
@article{https://doi.org/10.1002/jcaf.22179,
author = {Fanning, Kurt and Centers, David P.},
title = {Blockchain and Its Coming Impact on Financial Services},
journal = {Journal of Corporate Accounting \& Finance},
volume = {27},
number = {5},
pages = {53-57},
doi = {https://doi.org/10.1002/jcaf.22179},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jcaf.22179},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcaf.22179},
year = {2016}
}
%7
@ARTICLE{8936349,
  author={Zhaofeng, Ma and Lingyun, Wang and Xiaochang, Wang and Zhen, Wang and Weizhe, Zhao},
  journal={IEEE Internet of Things Journal}, 
  title={Blockchain-Enabled Decentralized Trust Management and Secure Usage Control of IoT Big Data}, 
  year={2020},
  volume={7},
  number={5},
  pages={4000-4015},
  keywords={Blockchain;Big Data;Trust management;Cloud computing;Internet of Things;Cryptography;Big data;blockchain;decentralized trust management;Internet of Things (IoT);secure usage control},
  doi={10.1109/JIOT.2019.2960526}}

%8
@inproceedings{10.1145/3442381.3449994,
author = {Yuan, Liang and He, Qiang and Tan, Siyu and Li, Bo and Yu, Jiangshan and Chen, Feifei and Jin, Hai and Yang, Yun},
title = {CoopEdge: A Decentralized Blockchain-based Platform for Cooperative Edge Computing},
year = {2021},
isbn = {9781450383127},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi-org.proxy.lib.duke.edu/10.1145/3442381.3449994},
doi = {10.1145/3442381.3449994},
booktitle = {Proceedings of the Web Conference 2021},
pages = {2245–2257},
numpages = {13},
keywords = {peer offloading, distributed consensus, cooperative edge computing, blockchain, Edge computing},
location = {Ljubljana, Slovenia},
series = {WWW '21}
}

%9
@ARTICLE{9103603,
  author={Zhuang, Peng and Zamir, Talha and Liang, Hao},
  journal={IEEE Transactions on Industrial Informatics}, 
  title={Blockchain for Cybersecurity in Smart Grid: A Comprehensive Survey}, 
  year={2021},
  volume={17},
  number={1},
  pages={3-19},
  keywords={Smart grids;Blockchain;Distributed ledger;Communication networks;Cyberattack;Distributed databases;Blockchain;cybersecurity;resiliency;smart contract;smart grid},
  doi={10.1109/TII.2020.2998479}}

%10 
@inproceedings{investopedia-uber-vs-yellowcabs,
  author = {CHRISTINA MAJASKI},
  title = {Uber vs. Yellow Cabs: A New York City Comparison},
  year={2024},
  url = {https://www.investopedia.com/articles/personal-finance/021015/uber-versus-yellow-cabs-new-york-city.asp},
 
}

%11
@inproceedings{model,
author = {Chao, Junzhi},
year = {2019},
month = {01},
pages = {},
title = {Modeling and Analysis of Uber’s Rider Pricing},
doi = {10.2991/aebmr.k.191217.127}
}

%12
@INPROCEEDINGS{9752864,
  author={Sindhu, Patchipulusu and Gupta, Diya and Meghana, Sanjeevi and M, Anand Kumar},
  booktitle={2022 IEEE Delhi Section Conference (DELCON)}, 
  title={Modeling Uber Data for Predicting Features Responsible for Price Fluctuations}, 
  year={2022},
  volume={},
  number={},
  pages={1-7},
  keywords={Fluctuations;Urban areas;Time series analysis;Predictive models;Multilayer perceptrons;Mobile applications;Public transportation;New York City Dataset;Data Analysis;Linear Regression;Decision Tree;Random Forest;Gradient Boost;Multilayer Perceptron},
  doi={10.1109/DELCON54057.2022.9752864}}

%13
@article{chen2016dynamic,
  title={Dynamic pricing in a labor market: Surge pricing and flexible work on the Uber platform.},
  author={Chen, M Keith and Sheldon, Michael},
  journal={Ec},
  volume={16},
  pages={455},
  year={2016}
}

%14==

%15
@INPROCEEDINGS{7037663,
  author={Biswas, Anshuman and Majumdar, Shikharesh and Nandy, Biswajit and El-Haraki, Ali},
  booktitle={2014 IEEE 6th International Conference on Cloud Computing Technology and Science}, 
  title={Automatic Resource Provisioning: A Machine Learning Based Proactive Approach}, 
  year={2014},
  volume={},
  number={},
  pages={168-173},
  keywords={Cloud computing;Maximum likelihood estimation;Resource management;Measurement;Training;Machine learning algorithms;Support vector machines;auto-scaling;resource allocation;dynamic resource provisioning;scheduling with SLAs;resource management on clouds},
  doi={10.1109/CloudCom.2014.147}}

%16
@ARTICLE{9040268,
  author={Luong, Nguyen Cong and Jiao, Yutao and Wang, Ping and Niyato, Dusit and Kim, Dong In and Han, Zhu},
  journal={IEEE Communications Magazine}, 
  title={A Machine-Learning-Based Auction for Resource Trading in Fog Computing}, 
  year={2020},
  volume={58},
  number={3},
  pages={82-88},
  keywords={Edge computing;Blockchain;Resource management;Biological system modeling;Integrated circuits;Cloud computing;Computational modeling;Machine learning},
  doi={10.1109/MCOM.001.1900136}}



%17
@Article{math11092212,
AUTHOR = {Butler, Conall and Crane, Martin},
TITLE = {Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods},
JOURNAL = {Mathematics},
VOLUME = {11},
YEAR = {2023},
NUMBER = {9},
ARTICLE-NUMBER = {2212},
URL = {https://www.mdpi.com/2227-7390/11/9/2212},
ISSN = {2227-7390},
DOI = {10.3390/math11092212}
}


%18
@article{zhang2023machine,
  title={Machine Learning for Blockchain: Literature Review and Open Research Questions},
  author={Zhang, Luyao},
  journal={OSF Preprints},
  year={2023},
  month={November},
  day={2},
  doi={10.31219/osf.io/g2q5t}
}

%20
@inproceedings{10.1145/3548606.3559341,
author = {Liu, Yulin and Lu, Yuxuan and Nayak, Kartik and Zhang, Fan and Zhang, Luyao and Zhao, Yinhong},
title = {Empirical Analysis of EIP-1559: Transaction Fees, Waiting Times, and Consensus Security},
year = {2022},
isbn = {9781450394505},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3548606.3559341},
doi = {10.1145/3548606.3559341},
booktitle = {Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security},
pages = {2099–2113},
numpages = {15},
keywords = {bounded rationality, causal inference, consensus security, eip-1559, empirical analysis, event studies, mechanism design, natural experiments, transaction fees, waiting time},
location = {Los Angeles, CA, USA},
series = {CCS '22}
}
%21
@inproceedings{guo2023spillover,
  author       = {Guo, Dezhen and Wang, Lizheng and Li, Yongjun},
  title        = {Spillover Effects of Airdrops: Evidence from Tokenization Platforms},
  booktitle    = {ICIS 2023 Proceedings},
  month        = {December 11},
  year         = {2023},
  url          = {https://aisel.aisnet.org/icis2023/user_behav/user_behav/7/}
}

%22
@misc{chung2022foundations,
      title={Foundations of Transaction Fee Mechanism Design}, 
      author={Hao Chung and Elaine Shi},
      year={2022},
      eprint={2111.03151},
      archivePrefix={arXiv},
      primaryClass={cs.GT}
}
%23
@misc{roughgarden2020transaction,
      title={Transaction Fee Mechanism Design for the Ethereum Blockchain: An Economic Analysis of EIP-1559}, 
      author={Tim Roughgarden},
      year={2020},
      eprint={2012.00854},
      archivePrefix={arXiv},
      primaryClass={cs.GT}
}
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
          and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
          and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
          Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
@misc{tensorflow2015-whitepaper,
title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
url={https://www.tensorflow.org/},
note={Software available from tensorflow.org},
author={
    Mart\'{i}n~Abadi and
    Ashish~Agarwal and
    Paul~Barham and
    Eugene~Brevdo and
    Zhifeng~Chen and
    Craig~Citro and
    Greg~S.~Corrado and
    Andy~Davis and
    Jeffrey~Dean and
    Matthieu~Devin and
    Sanjay~Ghemawat and
    Ian~Goodfellow and
    Andrew~Harp and
    Geoffrey~Irving and
    Michael~Isard and
    Yangqing Jia and
    Rafal~Jozefowicz and
    Lukasz~Kaiser and
    Manjunath~Kudlur and
    Josh~Levenberg and
    Dandelion~Man\'{e} and
    Rajat~Monga and
    Sherry~Moore and
    Derek~Murray and
    Chris~Olah and
    Mike~Schuster and
    Jonathon~Shlens and
    Benoit~Steiner and
    Ilya~Sutskever and
    Kunal~Talwar and
    Paul~Tucker and
    Vincent~Vanhoucke and
    Vijay~Vasudevan and
    Fernanda~Vi\'{e}gas and
    Oriol~Vinyals and
    Pete~Warden and
    Martin~Wattenberg and
    Martin~Wicke and
    Yuan~Yu and
    Xiaoqiang~Zheng},
  year={2015},
}

@misc{quan_wu_deng_zhang_2023,
title={Decoding Social Sentiment in DAO: A Comparative Analysis of Blockchain Governance Communities},
url={osf.io/bq6tu},
doi={10.31219/osf.io/bq6tu},
publisher={OSF Preprints},
author={Quan, Yutong and Wu, Xintong and Deng, Wanlin and Zhang, Luyao},
year={2023},
month={Oct}
}

@misc{zhang2024,
  author = { Wu, Xintong and Deng, Wanlin and Quan, Yutong and Zhang, Luyao},
  title = {From “Code is Law” to “Code and Law”: A Comparative Study on Blockchain Economics for China and the World},
  year = {2024},
  howpublished = {Summer Research Scholar by Sunshine},
  url = {https://srs.pubpub.org/pub/cscc2023/release/2}
}

@inproceedings{10.1145/3479722.3480991, 
author = {Ferreira, Matheus V. X. and Moroz, Daniel J. and Parkes, David C. and Stern, Mitchell}, 
title = {Dynamic posted-price mechanisms for the blockchain transaction-fee market}, 
year = {2021}, isbn = {9781450390828}, 
publisher = {Association for Computing Machinery}, 
address = {New York, NY, USA}, url = {https://doi.org/10.1145/3479722.3480991}, 
doi = {10.1145/3479722.3480991}, 
pages = {86–99}, 
numpages = {14}, 
location = {Arlington, Virginia}, series = {AFT '21} }


%new
@INPROCEEDINGS{9529485,
  author={Mars, Rawya and Abid, Amal and Cheikhrouhou, Saoussen and Kallel, Slim},
  booktitle={2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)}, 
  title={A Machine Learning Approach for Gas Price Prediction in Ethereum Blockchain}, 
  year={2021},
  volume={},
  number={},
  pages={156-165},
  keywords={Deep learning;Machine learning algorithms;Costs;Smart contracts;Predictive models;Logic gates;Prediction algorithms;Blockchain;Ethereum;Gas Price oracle;machine learning;Gas Mechanism},
  doi={10.1109/COMPSAC51774.2021.00033}}


@INPROCEEDINGS{9045840,
  author={Liu, Fangxiao and Wang, Xingya and Li, Zixin and Xu, Jiehui and Gao, Yubin},
  booktitle={2019 6th International Conference on Dependable Systems and Their Applications (DSA)}, 
  title={Effective GasPrice Prediction for Carrying Out Economical Ethereum Transaction}, 
  year={2020},
  volume={},
  number={},
  pages={329-334},
  keywords={Gases;Costs;Machine learning;Predictive models;Terms—Ethereum Smart Contract;Gas Mechanism;Gas Price Prediction;Machine Learning Regression},
  doi={10.1109/DSA.2019.00050}}


@proceedings{deep-bdb2021,
  title = {The International Conference on Deep Learning, Big Data and Blockchain (Deep-BDB 2021)},
  year = {2022},
  volume = {309},
  isbn = {978-3-030-84336-6},
  editor = {Chuang, Chih-Yun and Lee, Ting-Fang}
}

@article{muminov2024enhanced,
  author = {Muminov, Azamjon and Sattarov, Otabek and Na, Daeyoung},
  title = {Enhanced Bitcoin Price Direction Forecasting With DQN},
  journal = {IEEE Access},
  volume = {12},
  pages = {29093-29112},
  year = {2024}
}


@INPROCEEDINGS{9973526,
  author={Lan, Dongwan and Wang, Hao and Yin, Changchun and Zhou, Lu and Ge, Chunpeng and Lu, Xiaozhen},
  booktitle={2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)}, 
  title={Gas Price Prediction Based on Machine Learning Combined with Ethereum Mempool}, 
  year={2022},
  volume={},
  number={},
  pages={346-354},
  keywords={Learning systems;Machine learning;Pricing;Predictive models;Data models;Security;Proposals;Ethereum;Gas Price;Machine Learning;Prediction},
  doi={10.1109/MASS56207.2022.00057}}


@unknown{unknown,
author = {Augusto, André and Belchior, Rafael and Correia, Miguel and Vasconcelos, André and Zhang, Luyao and Hardjono, Thomas},
year = {2024},
month = {03},
pages = {},
title = {SoK: Security and Privacy of Blockchain Interoperability [Extended Version]},
doi = {10.36227/techrxiv.24595764.v2}
}


@InProceedings{10.1007/978-3-642-41947-8_4,
author="Raudys, Aistis
and Len{\v{c}}iauskas, Vaidotas
and Mal{\v{c}}ius, Edmundas",
editor="Skersys, Tomas
and Butleris, Rimantas
and Butkiene, Rita",
title="Moving Averages for Financial Data Smoothing",
booktitle="Information and Software Technologies",
year="2013",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="34--45",
abstract="For a long time moving averages has been used for a financial data smoothing. It is one of the first indicators in technical analysis trading. Many traders debated that one moving average is better than other. As a result a lot of moving averages have been created. In this empirical study we overview 19 most popular moving averages, create a taxonomy and compare them using two most important factors -- smoothness and lag. Smoothness indicates how much an indicator change (angle) and lag indicates how much moving average is lagging behind the current price. The aim is to have values as smooth as possible to avoid erroneous trades and with minimal lag -- to increase trend detection speed. This large-scale empirical study performed on 1850 real-world time series including stocks, ETF, Forex and futures daily data demonstrate that the best smoothness/lag ratio is achieved by the Exponential Hull Moving Average (with price correction) and Triple Exponential Moving Average (without correction).",
isbn="978-3-642-41947-8"
}

@article{liu2020certified,
  title={Certified monotonic neural networks},
  author={Liu, Xingchao and Han, Xing and Zhang, Na and Liu, Qiang},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={15427--15438},
  year={2020}
}
@article{kraaijeveld2020predictive,
  title={The predictive power of public Twitter sentiment for forecasting cryptocurrency prices},
  author={Kraaijeveld, Olivier and De Smedt, Johannes},
  journal={Journal of International Financial Markets, Institutions and Money},
  volume={65},
  pages={101188},
  year={2020},
  publisher={Elsevier}
}
@inproceedings{khan2022business,
  title={Business Intelligence Aspect for Emotions and Sentiments Analysis},
  author={Khan, Shakir},
  booktitle={2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)},
  pages={1--5},
  year={2022},
  organization={IEEE}
}
@inproceedings{mohapatra2019kryptooracle,
  title={KryptoOracle: a real-time cryptocurrency price prediction platform using twitter sentiments},
  author={Mohapatra, Shubhankar and Ahmed, Nauman and Alencar, Paulo},
  booktitle={2019 IEEE international conference on big data (Big Data)},
  pages={5544--5551},
  year={2019},
  organization={IEEE}
}

@article{milani2016fast,
  title={Fast and flexible monotonic functions with ensembles of lattices},
  author={Milani Fard, Mahdi and Canini, Kevin and Cotter, Andrew and Pfeifer, Jan and Gupta, Maya},
  journal={Advances in neural information processing systems},
  volume={29},
  year={2016}
}
@InProceedings{chen2023address,
  title = 	 {How to address monotonicity for model risk management?},
  author =       {Chen, Dangxing and Ye, Weicheng},
  booktitle = 	 {Proceedings of the 40th International Conference on Machine Learning},
  pages = 	 {5282--5295},
  year = 	 {2023},
  volume = 	 {202},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {23--29 Jul},
  publisher =    {PMLR}
}
@article{agarwal2021neural,
  title={Neural additive models: Interpretable machine learning with neural nets},
  author={Agarwal, Rishabh and Melnick, Levi and Frosst, Nicholas and Zhang, Xuezhou and Lengerich, Ben and Caruana, Rich and Hinton, Geoffrey E},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}
@article{klinker2011exponential,
  title={Exponential moving average versus moving exponential average},
  author={Klinker, Frank},
  journal={Mathematische Semesterberichte},
  volume={58},
  pages={97--107},
  year={2011},
  publisher={Springer}
}
@misc{ExponentialMovingAverage,
title={Exponential Moving Average (EMA)},
url={https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/ema}, publisher={fidelity} }


@article{hoepner2020significance,
  title={Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective},
  author={Hoepner, Andreas G. F. and McMillan, David and Vivian, Andrew and Wese Simen, Christel},
  journal={The European Journal of Finance},
  volume={27},
  number={1–2},
  pages={1--7},
  year={2020},
  publisher={Taylor \& Francis},
  doi={10.1080/1351847X.2020.1847725}
}

@ARTICLE{8290925,
  author={Liu, Qiang and Li, Pan and Zhao, Wentao and Cai, Wei and Yu, Shui and Leung, Victor C. M.},
  journal={IEEE Access}, 
  title={A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View}, 
  year={2018},
  volume={6},
  number={},
  pages={12103-12117},
  keywords={Security;Training;Machine learning algorithms;Training data;Support vector machines;Testing;Taxonomy;Machine learning;adversarial samples;security threats;defensive techniques},
  doi={10.1109/ACCESS.2018.2805680}}


@ARTICLE{9064510,
  author={Xue, Mingfu and Yuan, Chengxiang and Wu, Heyi and Zhang, Yushu and Liu, Weiqiang},
  journal={IEEE Access}, 
  title={Machine Learning Security: Threats, Countermeasures, and Evaluations}, 
  year={2020},
  volume={8},
  number={},
  pages={74720-74742},
  keywords={Machine learning;Security;Data models;Machine learning algorithms;Training;Training data;Prediction algorithms;Artificial intelligence security;poisoning attacks;backdoor attacks;adversarial examples;privacy-preserving machine learning},
  doi={10.1109/ACCESS.2020.2987435}}

@article{Andoni2019,
    title = {Blockchain technology in the energy sector: A systematic review of challenges and opportunities},
    author = {Merlinda Andoni and Valentin Robu and David Flynn and Simone Abram and Dale Geach and David Jenkins and Peter McCallum and Andrew Peacock},
    journal = {Renewable and Sustainable Energy Reviews},
    volume = {100},
    pages = {143-174},
    year = {2019},
    issn = {1364-0321},
    doi = {10.1016/j.rser.2018.10.014},
    url = {https://www.sciencedirect.com/science/article/pii/S1364032118307184}
}

@inproceedings{bct,
author = {Lawrenz, Sebastian and Sharma, Priyanka and Rausch, Andreas},
title = {Blockchain Technology as an Approach for Data Marketplaces},
year = {2019},
isbn = {9781450362689},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3320154.3320165},
doi = {10.1145/3320154.3320165},
pages = {55–59},
numpages = {5},
keywords = {Blockchain, Data marketplaces, Data quality, Security, Smart Contracts},
location = {, Honolulu, HI, USA, },
series = {ICBCT '19}
}