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[1] M. Dehmer,et al. An Introductory Review of Deep Learning for Prediction Models With Big Data , 2020, Frontiers in Artificial Intelligence.
[2] Peter Herrlich. The responsibility of the scientist , 2013, EMBO reports.
[3] John W. Sutherland,et al. Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data , 2019, Procedia CIRP.
[4] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[5] F Emmert-Streib,et al. Local network-based measures to assess the inferability of different regulatory networks. , 2010, IET systems biology.
[6] Chao Liu,et al. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults , 2019, Knowl. Based Syst..
[7] Mark S. Granovetter. Economic Action and Social Structure: The Problem of Embeddedness , 1985, American Journal of Sociology.
[8] J. Kvedar,et al. Artificial intelligence powers digital medicine , 2018, npj Digital Medicine.
[9] Matthias Dehmer,et al. Explainable artificial intelligence and machine learning: A reality rooted perspective , 2020, WIREs Data Mining Knowl. Discov..
[10] Mina J. Hanna,et al. User Data Privacy: Facebook, Cambridge Analytica, and Privacy Protection , 2018, Computer.
[11] Robert J. Kauffman,et al. Understanding the paradigm shift to computational social science in the presence of big data , 2014, Decis. Support Syst..
[12] Matthias Dehmer,et al. Ensuring Quality Standards and Reproducible Research for Data Analysis Services in Oncology: A Cooperative Service Model , 2019, Front. Cell Dev. Biol..
[13] A Min Tjoa,et al. Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI , 2018, CD-MAKE.
[14] Ron S. Jarmin,et al. Measuring the Digital Economy , 1999 .
[15] Kagermann Henning. Recommendations for implementing the strategic initiative INDUSTRIE 4.0 , 2013 .
[16] Hui Li,et al. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks , 2016, Journal of medical imaging.
[17] Yang Lu,et al. Industry 4.0: A survey on technologies, applications and open research issues , 2017, J. Ind. Inf. Integr..
[18] Yuxi Li,et al. Deep Reinforcement Learning: An Overview , 2017, ArXiv.
[19] Richard Heeks,et al. Defining, Conceptualising and Measuring the Digital Economy , 2017, International Organisations Research Journal.
[20] Timothy Baldwin,et al. An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation , 2016, Rep4NLP@ACL.
[21] Matthias Dehmer,et al. Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing , 2020, ArXiv.
[22] Weng Marc Lim,et al. The Sharing Economy: A Marketing Perspective , 2020, Australasian Marketing Journal.
[23] O. A. Sawy,et al. Digital business strategy: toward a next generation of insights , 2013 .
[24] E. Petricoin,et al. SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer. , 2004, Current opinion in biotechnology.
[25] Matthias Dehmer,et al. A clarification of misconceptions, myths and desired status of artificial intelligence , 2020, ArXiv.
[26] M. Kosinski,et al. Psychological targeting as an effective approach to digital mass persuasion , 2017, Proceedings of the National Academy of Sciences.
[27] Spyros Makridakis,et al. The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms , 2017 .
[28] Bill Franks,et al. Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics , 2012 .
[29] Honglak Lee,et al. Unsupervised feature learning for audio classification using convolutional deep belief networks , 2009, NIPS.
[30] Matthias Dehmer,et al. High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection , 2019, Mach. Learn. Knowl. Extr..
[31] V. Marx. Biology: The big challenges of big data , 2013, Nature.
[32] M. Turakhia,et al. Characteristics of Digital Health Studies Registered in ClinicalTrials.gov. , 2019, JAMA internal medicine.
[33] Mumbai,et al. Internet of Things (IoT): A Literature Review , 2015 .
[34] Antoine Grall,et al. Continuous-time predictive-maintenance scheduling for a deteriorating system , 2002, IEEE Trans. Reliab..
[35] Bradley Malin,et al. Anonymising and sharing individual patient data , 2015, BMJ : British Medical Journal.
[36] Mehmed Kantardzic,et al. Learning from Data , 2011 .
[37] Dhavan V. Shah,et al. Big Data, Digital Media, and Computational Social Science , 2015 .
[38] Dirk Helbing,et al. Thinking Ahead - Essays on Big Data, Digital Revolution, and Participatory Market Society , 2015, Springer International Publishing.
[39] Laura Shafner,et al. Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy , 2017, Stroke.
[40] P O Skobelev,et al. On the way from Industry 4.0 to Industry 5.0: from digital manufacturing to digital society , 2017 .
[41] Colin Lankshear,et al. Introduction: digital literacies: concepts, policies and practices , 2008 .
[42] Matthias Dehmer,et al. The Process of Analyzing Data is the Emergent Feature of Data Science , 2016, Front. Genet..
[43] Rob Kitchin,et al. The data revolution : big data, open data, data infrastructures & their consequences , 2014 .
[44] Eric J. Topol,et al. Digital medicine, on its way to being just plain medicine , 2018, npj Digital Medicine.
[45] Mónica Aguilar-Igartua,et al. Smart city for VANETs using warning messages, traffic statistics and intelligent traffic lights , 2012, 2012 IEEE Intelligent Vehicles Symposium.
[46] Alagan Anpalagan,et al. Efficient Energy Management for the Internet of Things in Smart Cities , 2017, IEEE Communications Magazine.
[47] Feng Xia,et al. From machine-to-machine communications towards cyber-physical systems , 2013, Comput. Sci. Inf. Syst..
[48] David Stuart,et al. The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences , 2015, Online Inf. Rev..
[49] Ronald W. Davis,et al. Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.
[50] Patty Kostkova,et al. Grand Challenges in Digital Health , 2015, Front. Public Health.
[51] Ronald P. S. Mahler,et al. Statistical Multisource-Multitarget Information Fusion , 2007 .
[52] M. Gerstein,et al. RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.
[53] Yu-Chee Tseng,et al. Pervasive and Mobile Computing ( ) – Pervasive and Mobile Computing Review from Wireless Sensor Networks towards Cyber Physical Systems , 2022 .
[54] Sergey Levine,et al. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[55] Yannis Charalabidis,et al. IoT and AI for Smart Government: A Research Agenda , 2019, Gov. Inf. Q..
[56] Gregory Cohen,et al. EMNIST: an extension of MNIST to handwritten letters , 2017, CVPR 2017.
[57] Matthias Dehmer,et al. Data Analytics Applications for Streaming Data From Social Media: What to Predict? , 2018, Front. Big Data.
[58] Veda C. Storey,et al. Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..
[59] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[60] Joy Bill,et al. Why the future doesn’t need us , 2003 .
[61] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[62] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[63] Ivan Stojmenovic,et al. Machine-to-Machine Communications With In-Network Data Aggregation, Processing, and Actuation for Large-Scale Cyber-Physical Systems , 2014, IEEE Internet of Things Journal.
[64] Mingming Cheng. Sharing economy: A review and agenda for future research , 2016 .
[65] Matthias Dehmer,et al. Applied Statistics for Network Biology: Methods in Systems Biology , 2011 .
[66] Dirk Helbing,et al. Societal, Economic, Ethical and Legal Challenges of the Digital Revolution: From Big Data to Deep Learning, Artificial Intelligence, and Manipulative Technologies , 2015, Towards Digital Enlightenment.
[67] Marina Jirotka,et al. Ethical governance is essential to building trust in robotics and artificial intelligence systems , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[68] Zoran Milosevic,et al. Ethics in Digital Health: A Deontic Accountability Framework , 2019, 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC).
[69] Yi Wang,et al. Industry 4.0: a way from mass customization to mass personalization production , 2017 .
[70] M. Braga,et al. Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..
[71] Matthias Dehmer,et al. Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error , 2019, Mach. Learn. Knowl. Extr..
[72] Yon Dohn Chung,et al. Utility-preserving anonymization for health data publishing , 2017, BMC Medical Informatics and Decision Making.
[73] Stephen Hailes,et al. Security of smart manufacturing systems , 2018 .
[74] Min Chen,et al. Machine-to-Machine Communications: Architectures, Standards and Applications , 2012, KSII Trans. Internet Inf. Syst..
[75] Gabriel-Miro Muntean,et al. A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches , 2015, IEEE Communications Surveys & Tutorials.
[76] Matthias Dehmer,et al. Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference , 2019, Mach. Learn. Knowl. Extr..
[77] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[78] Subhashini Venugopalan,et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.
[79] Li Da Xu,et al. Industry 4.0: state of the art and future trends , 2018, Int. J. Prod. Res..
[80] Max Welling,et al. Semi-supervised Learning with Deep Generative Models , 2014, NIPS.
[81] Detlef Schoder,et al. Consolidated, systemic conceptualization, and definition of the “sharing economy” , 2020, J. Assoc. Inf. Sci. Technol..
[82] D. Hoon,et al. Emerging technologies for studying DNA methylation for the molecular diagnosis of cancer , 2015, Expert review of molecular diagnostics.
[83] Andreas Holzinger,et al. Interactive machine learning for health informatics: when do we need the human-in-the-loop? , 2016, Brain Informatics.
[84] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[85] Rachel Botsman,et al. What's Mine Is Yours: The Rise of Collaborative Consumption , 2010 .
[86] Avi Ma'ayan,et al. Lean Big Data integration in systems biology and systems pharmacology. , 2014, Trends in pharmacological sciences.
[87] Jukka Huhtamäki,et al. Conceptualizing Big Social Data , 2017, Journal of Big Data.
[88] Francesco Buccafurri,et al. Comparing Twitter and Facebook user behavior: Privacy and other aspects , 2015, Comput. Hum. Behav..
[89] Dongyan Zhao,et al. Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges , 2019, NLPCC.
[90] Michael Marien,et al. Book Review: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies , 2014 .
[91] B. Ripley,et al. Pattern Recognition , 1968, Nature.
[92] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[93] J. Nadal,et al. Manifesto of computational social science , 2012 .
[94] 知秀 柴田. 5分で分かる!? 有名論文ナナメ読み:Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding , 2020 .
[95] Ernesto Damiani,et al. Augmented reality technologies, systems and applications , 2010, Multimedia Tools and Applications.
[96] Pierre Baldi,et al. Autoencoders, Unsupervised Learning, and Deep Architectures , 2011, ICML Unsupervised and Transfer Learning.
[97] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[98] M. Ienca,et al. Digital Medicine and Ethics: Rooting for Evidence , 2018, The American journal of bioethics : AJOB.
[99] Liping Zhu,et al. A Review on Dimension Reduction , 2013, International statistical review = Revue internationale de statistique.
[100] Wayne H. Wolf,et al. Cyber-physical Systems , 2009, Computer.
[101] Mathias Schmitt,et al. Human-machine-interaction in the industry 4.0 era , 2014, 2014 12th IEEE International Conference on Industrial Informatics (INDIN).
[102] Tomi Dufva,et al. Grasping the future of the digital society , 2019, Futures.
[103] Hanlee P. Ji,et al. Next-generation DNA sequencing , 2008, Nature Biotechnology.
[104] Allen Newell,et al. The psychology of human-computer interaction , 1983 .
[105] Matthias Dehmer,et al. Named Entity Recognition and Relation Detection for Biomedical Information Extraction , 2020, Frontiers in Cell and Developmental Biology.
[106] Amina Adadi,et al. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) , 2018, IEEE Access.
[107] K. Coombes,et al. Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology , 2009, 1010.1092.
[108] Araz Taeihagh,et al. Crowdsourcing, Sharing Economies and Development , 2017, ArXiv.
[109] J. Pope,et al. What's Mine is Yours , 2016, Journal of occupational and environmental medicine.
[110] Sahil R. Kalra,et al. Big Challenges? Big Data … , 2015 .