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Innovative informatics methods for process mining in health care

Journal of Biomedical informatics 2020年 109卷 103551-103551页

作者： Munoz-Gama, Jorge Martin, Niels Fernandez-Llatas, Carlos Johnson, Owen Sepúlveda, Marcos Department of Computer Science School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Research Group Business Informatics Hasselt University Hasselt Belgium Data Analytics Laboratory Vrije Universiteit Brussel Brussels Belgium ITACA Institute – Process Mining 4 Health Lab Universitat Politècnica de Valencia Valencia Spain Karolinska Institutet Stockholm Sweden School of Computing Leeds University Leeds United Kingdom

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CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

arXiv

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arXiv 2022年

作者： Nwoye, Chinedu Innocent Alapatt, Deepak Yu, Tong Vardazaryan, Armine Xia, Fangfang Zhao, Zixuan Xia, Tong Jia, Fucang Yang, Yuxuan Wang, Hao Yu, Derong Zheng, Guoyan Duan, Xiaotian Getty, Neil Sanchez-Matilla, Ricardo Robu, Maria Zhang, Li Chen, Huabin Wang, Jiacheng Wang, Liansheng Zhang, Bokai Gerats, Beerend Raviteja, Sista Sathish, Rachana Tao, Rong Kondo, Satoshi Pang, Winnie Ren, Hongliang Abbing, Julian Ronald Sarhan, Mohammad Hasan Bodenstedt, Sebastian Bhasker, Nithya Oliveira, Bruno Torres, Helena R. Ling, Li Gaida, Finn Czempiel, Tobias Vilaça, João L. Morais, Pedro Fonseca, Jaime Egging, Ruby Mae Wijma, Inge Nicole Qian, Chen Bian, Guibin Li, Zhen Balasubramanian, Velmurugan Sheet, Debdoot Luengo, Imanol Zhu, Yuanbo Ding, Shuai Aschenbrenner, Jakob-Anton van der Kar, Nicolas Elini Xu, Mengya Islam, Mobarakol Seenivasan, Lalithkumar Jenke, Alexander Stoyanov, Danail Mutter, Didier Mascagni, Pietro Seeliger, Barbara Gonzalez, Cristians Padoy, Nicolas ICube University of Strasbourg CNRS France IHU Strasbourg France Department of Computer Science University of Chicago United States Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Management Hefei University of Technology Hefei China Institue of Medical Robotics Shanghai Jiao Tong University Shanghai China Argonne National Laboratory 9700 S Cass Ave LemontIL60439 United States Digital Surgery A Medtronic Company London United Kingdom Institute of Automation Chinese Academy of Sciences China Department of Computer Science School of Informatics Xiamen University Xiamen China Johnson & Johnson Meander Medical Centre Netherlands Indian Institute of Technology Kharagpur India Muroran Institute of Technology Japan Department of Biomedical Engineering National University of Singapore Singapore Department for Translational Surgical Oncology National Center for Tumor Diseases Partner Site Dresden Germany 2Ai School of Technology IPCA Barcelos Portugal School of Medicine University of Minho Braga Portugal Algoritimi Center School of Engineering University of Minho Guimeraes Portugal Technical University of Munich Germany Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Wellcome EPSRC Center for Interventional and Surgical Science University College London United Kingdom Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy University Hospital of Strasbourg France

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity;yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery. © 2022, CC BY-NC-SA.

关键词： Deep learning

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computer aided diagnosis system for liver cirrhosis based on ultrasound images 18

Computer aided diagnosis system for liver cirrhosis based on...

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7th International Conference on Software and Information Engineering, ICSIE 2018

作者： Rabie, Reham Eltoukhy, Mohamed Meselhy Al-Shatouri, Mohammad Rashed, Essam A. Image Science Lab. Dept. of Math. Faculty of Science Suez Canal University Ismailia Egypt Faculty of Computers and Informatics Suez Canal University Ismailia Egypt Dept. of Radiology Faculty of Medicine Suez Canal University Ismailia Egypt Faculty of Informatics and Computer Science British University in Egypt Cairo Egypt

ISBN: (纸本)9781450364690

This work introduces a computer-aided diagnosis (CAD) system for diagnosing liver cirrhosis in ultrasound (US) images. The proposed system uses a set of features obtained from different feature extraction methods. These features are the first order statistics (FOS), the fractal dimension (FD), the gray level co-occurrence matrix (GLCM), the Gabor filter (GF), the wavelet (WT) and the curvelet (CT) features. The measured features are presented in two different classifiers such as support vector machine (SVM) and k-nearest neighbors (K-NN). The proposed system is applied on dataset consists of 72 cirrhosis and 75 normal regions each of 128128 pixels. The classification accuracy rates are calculated using a 10-fold cross validation. A correlation-based feature selection (CFS) is used resulting in better accuracy predictions. The results showed that SVM and K-NN classifiers achieved higher performance with the combination of the wavelet and curvelet feature vectors than other feature extraction methods. © 2018 Association for Computing Machinery.

关键词： Support vector machines

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Using User Generated Content in Mobile Application to Support Children with Special Needs 3

Using User Generated Content in Mobile Application to Suppor...

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3rd International Conference on Information Technology, InCIT 2018

作者： Kaenampornpan, Manasawee Banluewong, Kaveepoj Gorman, Nicholas John Upapong, Wutcharaporn Kon-Roo, Patumwan Polar lab Department of Computer Science Faculty of Informatics Mahasarakham University Mahasarakham Thailand Khon Kaen University Demonstration School Faculty of Education Khon Kaen University Khon Kaen Thailand

ISBN: (纸本)9789741960323

Children with special needs are able to learn and live their life with normal people. They have certain symptoms that might affect their performance in daily life. Parents and teachers are an important part of the child's life. They have to find the way to get child's attention by creating content and media that suitable for them. This could be time consuming and costly. As mobile devices become more accessible to everyone. We propose an application that contains 2D animation of instructional media and interactive games. Moreover, based on the user generated content concept, we allow the parents and teachers to generate content with mobile devices' functionalities. The content is then fresh and more relevant to each particular student. We tested the application with 15 parents and teachers. After they tested the application with their children, the result from the questionnaire shows that 33 percents of users are very satisfied with the application while the remaining 67 percents satisfied with the application. The parents and teachers gave a high satisfaction towards its beneficial with an overall average at 4.6. They would like to use the application again. © 2018 Mahasarakham University, Faculty of informatics.

关键词： Mobile computing

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Objective smoking: Towards smoking detection using smartwatch sensors 3rd

Objective smoking: Towards smoking detection using smartwatc...

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3rd International Conference on Biomedical and Health informatics, ICBHI 2017

作者： Maramis, C. Kilintzis, V. Scholl, P. Chouvarda, I. Lab of Computing & Medical Informatics Department of Medicine Aristotle University of Thessaloniki KEDIP Building University Campus Thessaloniki Greece Embedded Systems Department of Computer Science University of Freiburg Freiburg Germany

ISBN: (纸本)9789811074189

Smoking is arguably one of the most harmful lifestyle behaviors with well-known relations to dozens of diseases. As a consequence, smoking monitoring and cessation support have been popular targets for the emerging field of Behavioral informatics. Although smoking monitoring has been attempted in the past (e.g., self-reporting), the revolution of smart wearable devices has provided the necessary tools for developing truly objective and unobtrusive smoking detection methods applicable in real-life settings. This work takes the first step towards the development of such a method using smartwatch inertial sensory data, with the end-goal of integrating the proposed method in a novel just-in-time intervention for smoking cessation. The paper explores the detection of smoking instances and the constituting puffs from 3-axis gyroscope data that are routinely acquired by any Android Wear smartphone. The feasibility of the method is tested on actual real-life data, yielding very promising preliminary results. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Android (operating system)

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PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery

arXiv

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arXiv 2024年

作者： Das, Adrito Khan, Danyal Z. Psychogyios, Dimitrios Zhang, Yitong Hanrahan, John G. Vasconcelos, Francisco Pang, You Chen, Zhen Wu, Jinlin Zou, Xiaoyang Zheng, Guoyan Qayyum, Abdul Mazher, Moona Razzak, Imran Li, Tianbin Ye, Jin He, Junjun Plotka, Szymon Kaleta, Joanna Yamlahi, Amine Jund, Antoine Godau, Patrick Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Rivoir, Dominik Pérez, Alejandra Rodriguez, Santiago Arbeláez, Pablo Stoyanov, Danail Marcus, Hani J. Bano, Sophia Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department of Neurosurgery National Hospital for Neurology and Neurosurgery London United Kingdom HKISI CAS China Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China National Heart and Lung Institute Faculty of Medicine Imperial College London United Kingdom Centre for Medical Image Computing University College London London United Kingdom University of New South Wales Sydney Australia Shanghai AI Lab Shanghai China Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center University of Amsterdam Amsterdam Netherlands Sano Center for Computational Medicine Krakow Poland Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg a partnership between DKFZ University Hospital Heidelberg Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Muroran Institute of Technology Hokkaido Japan Niigata University of Health and Welfare Niigata Japan Konica Minolta Inc. Osaka Japan National Center for Tumor Diseases Dresden Germany Centre for Tactile Internet TUD Dresden Germany Universidad de los Andes Bogota Colombia DKFZ UKDD TUD Germany

The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed;and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery;during live surgery;and when writing operation notes. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a unique task when compared to other minimally invasive surgeries due to the smaller working space, which limits and distorts vision;and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. A commonality between the top performing models was incorporating spatio-temporal and multi-task methods, with greater than 50% and 10% macro-F1-score improvement over purely spacial single-task models in step and instrument recognition respectively. The PitVis-2023 Challenge therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset, with surgery specific techniques used to enhance performance, progressing the field further. Benchmark results are provided in the paper, and the dataset is publicly available at: https://***/10.5522/04/26531686. Copyright © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology and Evolution 2025年第6期9卷 1031-1042页

作者： Pringle, Stephen Dallimer, Martin Goddard, Mark A. Le Goff, Léni K. Hart, Emma Langdale, Simon J. Fisher, Jessica C. Abad, Sara-Adela Ancrenaz, Marc Angeoletto, Fabio Auat Cheein, Fernando Austen, Gail E. Bailey, Joseph J. Baldock, Katherine C. R. Banin, Lindsay F. Banks-Leite, Cristina Barau, Aliyu S. Bashyal, Reshu Bates, Adam J. Bicknell, Jake E. Bielby, Jon Bosilj, Petra Bush, Emma R. Butler, Simon J. Carpenter, Dan Clements, Christopher F. Cully, Antoine Davies, Kendi F. Deere, Nicolas J. Dodd, Michael Drinkwater, Rosie Driscoll, Don A. Dutilleux, Guillaume Dyrmann, Mads Edwards, David P. Farhadinia, Mohammad S. Faruk, Aisyah Field, Richard Fletcher, Robert J. Foster, Chris W. Fox, Richard Francksen, Richard M. Franco, Aldina M. A. Gainsbury, Alison M. Gardner, Charlie J. Giorgi, Ioanna Griffiths, Richard A. Hamaza, Salua Hanheide, Marc Hayward, Matt W. Hedblom, Marcus Helgason, Thorunn Heon, Sui P. Hughes, Kevin A. Hunt, Edmund R. Ingram, Daniel J. Jackson-Mills, George Jowett, Kelly Keitt, Timothy H. Kloepper, Laura N. Kramer-Schadt, Stephanie labisko, Jim labrosse, Frédéric Lawson, Jenna Lecomte, Nicolas de Lima, Ricardo F. Littlewood, Nick A. Marshall, Harry H. Masala, Giovanni L. Maskell, Lindsay C. Matechou, Eleni Mazzolai, Barbara McConnell, Alistair Melbourne, Brett A. Miriyev, Aslan Nana, Eric Djomo Ossola, Alessandro Papworth, Sarah Parr, Catherine L. Payo-Payo, Ana Perry, Gad Pettorelli, Nathalie Pillay, Rajeev Potts, Simon G. Prendergast-Miller, Miranda T. Qie, Lan Rolley-Parnell, Persie Rossiter, Stephen J. Rowcliffe, Marcus Rumble, Heather Sadler, Jon P. Sandom, Christopher J. Sanyal, Asiem Schrodt, Franziska Sethi, Sarab S. Shabrani, Adi Siddall, Robert Smith, Simón C. Snep, Robbert P. H. Soulsbury, Carl D. Stanley, Margaret C. Stephens, Philip A. Stephenson, P.J. Struebig, Matthew J. Studley, Matthew Svátek, Martin Tang, Gilbert Taylor, Nicholas K. Umbers, Kate D. L. Ward, Robert J. White, Patrick J. C. Whittingham, Mark J. Wich, Serge Williams, Christopher D. Yakubu, Ibrahim B. Yoh, Natalie Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury United Kingdom Centre for Environmental Policy Imperial College London London United Kingdom Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Synthotech Ltd Milner Court Hornbeam Square Harrogate United Kingdom Mechanical Engineering Department University College London London United Kingdom HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brazil Department of Engineering Harper Adams University Newport United Kingdom Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge United Kingdom Centre for Ecology and Hydrology Penicuik United Kingdom Department of Life Sciences Imperial College London Silwood Park Campus Ascot United Kingdom Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire United Kingdom School of Biological and Environmental Sciences Liverpool John Moores University Liverpool United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Royal Botanic Garden Edinburgh Edinburgh United Kingdom School of Biological Sciences University of East Anglia Norwich Research Park Norwich United Kingdom Digital Ecology Limited Bristol United Kingdom School of Biological Sciences University of Bristol Bristol United Kingdom Department of Computing Imperial College London London United Kingdom Department of Ecology and Evolutionary Biology University of Colorado Boulder CO United States Faculty of Science Technology Engineering

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively. © The Author(s) 2025.

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Shared Metadata for Data-Centric Materials science

arXiv

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arXiv 2022年

作者： Ghiringhelli, Luca M. Baldauf, Carsten Bereau, Tristan Brockhauser, Sandor Carbogno, Christian Chamanara, Javad Cozzini, Stefano Curtarolo, Stefano Draxl, Claudia Dwaraknath, Shyam Fekete, Ádám Kermode, James Koch, Christoph T. Kühbach, Markus Ladines, Alvin Noe Lambrix, Patrick Lenz-Himmer, Maja-Olivia Levchenko, Sergey Oliveira, Micael Michalchuk, Adam Miller, Ron Onat, Berk Pavone, Pasquale Pizzi, Giovanni Regler, Benjamin Rignanese, Gian-Marco Schaarschmidt, Jörg Scheidgen, Markus Schneidewind, Astrid Sheveleva, Tatyana Su, Chuanxun Usvyat, Denis Valsson, Omar Wöll, Christof Scheffler, Matthias Physics Department IRIS Adlershof Humboldt-Universität zu Berlin Germany The NOMAD Lab The Fritz Haber Institute The Max Planck Society Humboldt University Berlin Germany Fritz Haber Institute of the Max Planck Society Berlin Germany Van’t Hoff Institute for Molecular Sciences Informatics Institute University of Amsterdam Amsterdam1098 XH Netherlands TIB Leibniz Information Centre for Science and Technology University Library Welfengarten 1B Hanover30167 Germany AREA Science Park località Padriciano Trieste34149 Italy Center for Autonomous Materials Design Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Lawrence Berkeley National Laboratory BerkeleyCA United States Warwick Centre for Predictive Modelling School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Department of Computer and Information Science Linköping University Sweden The Swedish e-Science Research Centre Linköping University Sweden Max Planck Institute for the Structure and Dynamics of Matter Hamburg Germany Berlin12489 Germany Department of Mechanical and Aerospace Engineering Carleton University OttawaONK1S 5B6 Canada École Polytechnique Fédérale de Lausanne LausanneCH-1015 Switzerland UCLouvain Chemin des Étoiles 8 Louvain-la-NeuveB-1348 Belgium Eggenstein-Leopoldshafen76344 Germany Jülich Center for Neutron Science MLZ Forschungszentrum Jülich GmbH Lichtenbergstraße 1 Garching85748 Germany CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Chemistry Department Humboldt-Universität zu Berlin Germany Department of Chemistry University of North Texas DentonTX76201 United States Eggenstein-Leopoldshafen76344 Germany

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on "Shared Metadata and Data Formats for Big-Data Driven Materials science". We start from an operative definition of metadata, and what features a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them. © 2022, CC BY.

关键词： Metadata

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Making sense of audio vibration for liquid height estimation in robotic pouring

arXiv

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arXiv 2019年

作者： Liang, Hongzhuo Li, Shuang Ma, Xiaojian Hendrich, Norman Gerkmann, Timo Sun, Fuchun Zhang, Jianwei Department of Informatics Universität Hamburg Germany Center for Vision Cognition Learning and Autonomy Department of Statistics University of California Los Angeles United States State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Department of Informatics Universität Hamburg Germany

In this paper, we focus on the challenging perception problem in robotic pouring. Most of the existing approaches either leverage visual or haptic information. However, these techniques may suffer from poor generalization performances on opaque containers or concerning measuring precision. To tackle these drawbacks, we propose to make use of audio vibration sensing and design a deep neural network PouringNet to predict the liquid height from the audio fragment during the robotic pouring task. PouringNet is trained on our collected real-world pouring dataset with multimodal sensing data, which contains more than 3000 recordings of audio, force feedback, video and trajectory data of the human hand that performs the pouring task. Each record represents a complete pouring procedure. We conduct several evaluations on PouringNet with our dataset and robotic hardware. The results demonstrate that our PouringNet generalizes well across different liquid containers, positions of the audio receiver, initial liquid heights and types of liquid, and facilitates a more robust and accurate audio-based perception for robotic pouring. Copyright © 2019, The Authors. All rights reserved.

关键词： Containers

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An Overview of Cyber Insecurity and Malicious Uses of Cyberspace 4th

An Overview of Cyber Insecurity and Malicious Uses of Cybers...

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4th International Conference on Advanced Engineering Theory and Applications, AETA 2017

作者： Silva, Rui Miguel Zelinka, Ivan Lab UbiNET – Computer Science Security and Cybercrime/LISP – Laboratory of Informatics Systems and Parallelism Superior and Management School Polytechnic Institute of Beja Rua Pedro Soares s/n Beja7800-295 Portugal Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB-TUO 17. Listopadu 2172/15 Ostrava-Poruba 708 00 Czech Republic

ISBN: (纸本)9783319698137

Cyberspace entered in our lives for years, in all the domain areas of modern societies, since gamming and fun, from sport information to political and cultural news, scientific dissemination, governmental and public duty activities and all of these domains create a new economical and financial really. Nowadays "People and Money" are on cyberspace and these turns the focus of crime activities to cyberspace. By other side, cyberspace gives fast access from one corner of the world to other corners of the world just in a mouse click and all these with physical cover. So, cybercrime, and all the other "cybers", like cyber security, cyber defense, cyber warfare, cyber terrorism, cyber hacktivism, or cyber spy, came to stay in the real world. The talk will overview the state of the art of the bad uses or malicious uses of cyberspace due to insecurity and benefits form cyberspace as a platform for malicious activities. © Springer International Publishing AG 2018.

关键词： Cybersecurity

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