Pub Date : 1900-01-01DOI: 10.1007/978-3-031-24349-3_18
Bettina Fazzinga, René Mellema
{"title":"Argumentation in AI","authors":"Bettina Fazzinga, René Mellema","doi":"10.1007/978-3-031-24349-3_18","DOIUrl":"https://doi.org/10.1007/978-3-031-24349-3_18","url":null,"abstract":"","PeriodicalId":348804,"journal":{"name":"International Conference on Advances in Computing and Artificial Intelligence","volume":"41 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":"114256322","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}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-24349-3_16
Leila Methnani, M. Brännström, Andreas Theodorou
{"title":"Operationalising AI Ethics: Conducting Socio-technical Assessment","authors":"Leila Methnani, M. Brännström, Andreas Theodorou","doi":"10.1007/978-3-031-24349-3_16","DOIUrl":"https://doi.org/10.1007/978-3-031-24349-3_16","url":null,"abstract":"","PeriodicalId":348804,"journal":{"name":"International Conference on Advances in Computing and Artificial Intelligence","volume":"18 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":"123282556","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}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-24349-3_4
J. Crowley
{"title":"Generative Networks and the AutoEncoder","authors":"J. Crowley","doi":"10.1007/978-3-031-24349-3_4","DOIUrl":"https://doi.org/10.1007/978-3-031-24349-3_4","url":null,"abstract":"","PeriodicalId":348804,"journal":{"name":"International Conference on Advances in Computing and Artificial Intelligence","volume":"48 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":"127499877","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}
Due to the rise in popularity of Bitcoin as both a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools to gain an advantage over the market. Although traditionally trading was done by professionals, nowadays a majority of market participants are market-data processing bots due to their inherent advantages in processing large amounts of data, lack of emotions of fear or greed, and predicting market prices through artificial intelligence. A large number of approaches have been brought forward to tackle this task, many of which rely on specially engineered deep learning methods with a focus on specific market conditions. The general limitation of these approaches, however, is the reliance on customized gradient-based methods which limit the scope of possible solutions and don't necessarily generalize well when solving similar problems. This paper proposes a method which uses neuroevolutionary techniques capable of automatically customizing offspring neural networks, generating entire populations of solutions and more thoroughly exploring and parallelizing potential solutions. Our approach uses evolutionary algorithms to evolve increasingly improved populations of neural networks which, based on sentimental and technical analysis data, efficiently predict future market price movements. The effectiveness of this approach is validated by testing the system on both live and historical trading scenarios, and its robustness is tested on other cryptocurrency and stock markets. Experimental results during a 30-day live-trading period show that this method outperformed the buy and hold strategy by over 260%, even while factoring in standard trading fees.
{"title":"EvoTrader: Automated Bitcoin Trading Using Neuroevolutionary Algorithms on Technical Analysis and Social Sentiment Data","authors":"Martin Pellon Consunji","doi":"10.1145/3508546.3508652","DOIUrl":"https://doi.org/10.1145/3508546.3508652","url":null,"abstract":"Due to the rise in popularity of Bitcoin as both a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools to gain an advantage over the market. Although traditionally trading was done by professionals, nowadays a majority of market participants are market-data processing bots due to their inherent advantages in processing large amounts of data, lack of emotions of fear or greed, and predicting market prices through artificial intelligence. A large number of approaches have been brought forward to tackle this task, many of which rely on specially engineered deep learning methods with a focus on specific market conditions. The general limitation of these approaches, however, is the reliance on customized gradient-based methods which limit the scope of possible solutions and don't necessarily generalize well when solving similar problems. This paper proposes a method which uses neuroevolutionary techniques capable of automatically customizing offspring neural networks, generating entire populations of solutions and more thoroughly exploring and parallelizing potential solutions. Our approach uses evolutionary algorithms to evolve increasingly improved populations of neural networks which, based on sentimental and technical analysis data, efficiently predict future market price movements. The effectiveness of this approach is validated by testing the system on both live and historical trading scenarios, and its robustness is tested on other cryptocurrency and stock markets. Experimental results during a 30-day live-trading period show that this method outperformed the buy and hold strategy by over 260%, even while factoring in standard trading fees.","PeriodicalId":348804,"journal":{"name":"International Conference on Advances in Computing and Artificial Intelligence","volume":"11 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":"123846429","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}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-24349-3_11
Stefan Buijsman
{"title":"Why and How Should We Explain AI?","authors":"Stefan Buijsman","doi":"10.1007/978-3-031-24349-3_11","DOIUrl":"https://doi.org/10.1007/978-3-031-24349-3_11","url":null,"abstract":"","PeriodicalId":348804,"journal":{"name":"International Conference on Advances in Computing and Artificial Intelligence","volume":"97 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":"116155277","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}
In current computer-aided diagnostic systems, existing detection functions are usually only for a specific type of lesion, such as skin lesions, pulmonary nodule lesions and liver lesions. However, in actual clinical diagnosis, many lesions are actually related. For example, pulmonary nodular lesions may metastasize and spread to lymph node areas or other body parts. The detection of a single site is not conducive to the doctor's comprehensive diagnosis of the condition. Multi-site lesion detection can detect lesion metastasis and treat it earlier, and can also explore the relationship between different lesions. In response to this situation, this thesis uses the Deeplesion dataset to establish a general lesion detection framework that can detect possible lesions through CT images of different parts of the body. Compared with the existing single-path computer-aided diagnosis system, this thesis studies and implements a general lesion detection system to explore the relationship between different lesions. This will help doctors to make a comprehensive clinical diagnosis and visualize the results. This thesis based on the Faster R-CNN network model. First it denoises and enhances the CT images. Then, the VGG16 network is used for feature extraction, and the feature map is obtained through the RPN network to obtain candidate suggestion regions. In view of the misdetection of missed detection, this thesis introduces a Gaussian weighted penalty function to improve the non-maximum suppression. Finally, Tkinter is used to create a GUI visualization interface for doctors to compare clinical diagnosis.
{"title":"Generalized Lesion Detector Based on Convolutional Neural Network","authors":"Hao Wu, Jian-Zhi Deng","doi":"10.1145/3377713.3377746","DOIUrl":"https://doi.org/10.1145/3377713.3377746","url":null,"abstract":"In current computer-aided diagnostic systems, existing detection functions are usually only for a specific type of lesion, such as skin lesions, pulmonary nodule lesions and liver lesions. However, in actual clinical diagnosis, many lesions are actually related. For example, pulmonary nodular lesions may metastasize and spread to lymph node areas or other body parts. The detection of a single site is not conducive to the doctor's comprehensive diagnosis of the condition. Multi-site lesion detection can detect lesion metastasis and treat it earlier, and can also explore the relationship between different lesions. In response to this situation, this thesis uses the Deeplesion dataset to establish a general lesion detection framework that can detect possible lesions through CT images of different parts of the body. Compared with the existing single-path computer-aided diagnosis system, this thesis studies and implements a general lesion detection system to explore the relationship between different lesions. This will help doctors to make a comprehensive clinical diagnosis and visualize the results. This thesis based on the Faster R-CNN network model. First it denoises and enhances the CT images. Then, the VGG16 network is used for feature extraction, and the feature map is obtained through the RPN network to obtain candidate suggestion regions. In view of the misdetection of missed detection, this thesis introduces a Gaussian weighted penalty function to improve the non-maximum suppression. Finally, Tkinter is used to create a GUI visualization interface for doctors to compare clinical diagnosis.","PeriodicalId":348804,"journal":{"name":"International Conference on Advances in Computing and Artificial Intelligence","volume":"95 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":"114337436","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}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-24349-3_13
G. Boella, M. Mori
{"title":"An Introduction to Ethics and AI","authors":"G. Boella, M. Mori","doi":"10.1007/978-3-031-24349-3_13","DOIUrl":"https://doi.org/10.1007/978-3-031-24349-3_13","url":null,"abstract":"","PeriodicalId":348804,"journal":{"name":"International Conference on Advances in Computing and Artificial Intelligence","volume":"23 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":"128264350","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}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-24349-3_3
J. Crowley
{"title":"Machine Learning with Neural Networks","authors":"J. Crowley","doi":"10.1007/978-3-031-24349-3_3","DOIUrl":"https://doi.org/10.1007/978-3-031-24349-3_3","url":null,"abstract":"","PeriodicalId":348804,"journal":{"name":"International Conference on Advances in Computing and Artificial Intelligence","volume":"37 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":"115147137","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}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-24349-3_19
Emmanuelle-Anna Dietz, A. Kakas, Loizos Michael
{"title":"Computational Argumentation & Cognitive AI","authors":"Emmanuelle-Anna Dietz, A. Kakas, Loizos Michael","doi":"10.1007/978-3-031-24349-3_19","DOIUrl":"https://doi.org/10.1007/978-3-031-24349-3_19","url":null,"abstract":"","PeriodicalId":348804,"journal":{"name":"International Conference on Advances in Computing and Artificial Intelligence","volume":"127 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":"128805546","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}
Pub Date : 1900-01-01DOI: 10.1007/978-3-031-24349-3_14
M. Hildebrandt, Arno De Bois
{"title":"Law for Computer Scientists","authors":"M. Hildebrandt, Arno De Bois","doi":"10.1007/978-3-031-24349-3_14","DOIUrl":"https://doi.org/10.1007/978-3-031-24349-3_14","url":null,"abstract":"","PeriodicalId":348804,"journal":{"name":"International Conference on Advances in Computing and Artificial Intelligence","volume":"56 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":"133794646","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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