Full Issue

Editorial

object(Publication)#85 (6) { ["_data"]=> array(28) { ["id"]=> int(7684) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2024-06-28" ["lastModified"]=> string(19) "2024-07-18 11:25:01" ["primaryContactId"]=> int(9660) ["sectionId"]=> int(143) ["seq"]=> int(1) ["submissionId"]=> int(7560) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(0) "" ["copyrightYear"]=> int(2024) ["issueId"]=> int(594) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(3) "3-8" ["pub-id::doi"]=> string(21) "10.32575/ppb.2024.1.1" ["abstract"]=> array(1) { ["en_US"]=> string(16) "

editorial

" } ["copyrightHolder"]=> array(1) { ["en_US"]=> string(55) "Hojjat Adeli, Makó Csaba, Kis Norbert, Török Bernát" } ["title"]=> array(1) { ["en_US"]=> string(110) "Editors’ Note: Introduction to the Thematic Issue on Responsible Artificial Intelligence and Platform Labour" } ["locale"]=> string(5) "en_US" ["authors"]=> array(4) { [0]=> object(Author)#815 (6) { ["_data"]=> array(15) { ["id"]=> int(9660) ["email"]=> string(20) "dul.janos@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(7684) ["seq"]=> int(1) ["userGroupId"]=> int(116) ["country"]=> string(2) "US" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(6) "Hojjat" ["hu_HU"]=> string(6) "Hojjat" } ["givenName"]=> array(2) { ["en_US"]=> string(5) "Adeli" ["hu_HU"]=> string(5) "Adeli" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [1]=> object(Author)#845 (6) { ["_data"]=> array(15) { ["id"]=> int(9661) ["email"]=> string(20) "dul.janos@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(7684) ["seq"]=> int(1) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(5) "Makó" ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(5) "Csaba" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [2]=> object(Author)#843 (6) { ["_data"]=> array(15) { ["id"]=> int(9662) ["email"]=> string(22) "kis.norbert@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(7684) ["seq"]=> int(1) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(3) "Kis" ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(7) "Norbert" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [3]=> object(Author)#792 (6) { ["_data"]=> array(15) { ["id"]=> int(9663) ["email"]=> string(23) "torok.bernat@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(7684) ["seq"]=> int(1) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(7) "Török" ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(7) "Bernát" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(1) { ["en_US"]=> array(1) { [0]=> string(9) "editorial" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#842 (7) { ["_submissionFile"]=> NULL ["_data"]=> array(9) { ["submissionFileId"]=> int(34644) ["id"]=> int(5945) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(7684) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }
PDF

Studies

object(Publication)#165 (6) { ["_data"]=> array(28) { ["id"]=> int(7056) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2024-06-28" ["lastModified"]=> string(19) "2024-07-17 10:29:10" ["primaryContactId"]=> int(8750) ["sectionId"]=> int(17) ["seq"]=> int(1) ["submissionId"]=> int(6932) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(19794) "AL-SARTAWI, Abdalmuttaleb M. A. Musleh ed. (2021): Big Data-Driven Digital Economy: Artificial and Computational Intelligence. Springer. Online: https://doi.org/10.1007/978-3-030-73057-4 BAI, Junwei – CHENG, Chi (2020): A Social Network Image Classification Algorithm Based on Multimodal Deep Learning. International Journal Computers Communications and Control, 15(6). Online: https://doi.org/10.15837/ijccc.2020.6.4037 BAI, Zhongbo – BAI, Xiaomei (2022): Towards Understanding the Analysis, Models, and Future Directions of Sports Social Networks. Complexity, 5743825. Online: https://doi.org/10.1155/2022/5743825 BAND, Shahab S. – ARDABILI, Sina – SOOKHAK, Mehdi – CHRONOPOULOS, Anthony T. – ELNAFFAR, Said – MOSLEHPOUR, Massoud et al. (2022): When Smart Cities Get Smarter via Machine Learning: An In-Depth Literature Review. IEEE Access, 10, 60985–61015. Online: https://doi.org/10.1109/ACCESS.2022.3181718 Buyalskaya, A. –Gallo, M., –Camerer, C. F. (2021). The golden age of social science. Proceedings of the National Academy of Sciences, 118(5), https://doi.org/10.1073/pnas.2002923118 BI, Zhongpin – JING, Lina – SHAN, Meijing – DOU, Shuming – WANG, Shiyang (2021): Hierarchical Social Recommendation Model Based on a Graph Neural Network. Wireless Communications and Mobile Computing, 2021. Online: https://doi.org/10.1155/2021/9107718 CHOI, Daejin – Oh, Hyuncheol – Chun, Selin – Kwon, Taekyoung – Han, Jinyoung (2022): Preventing Rumor Spread with Deep Learning. Expert Systems with Applications, 197, 116688. Online: https://doi.org/10.1016/j.eswa.2022.116688 CHOI, Hwiyong et al. (2018): Classification of Noise between Floors in a Building Using Pre-Trained Deep Convolutional Neural Networks. In 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE. Online: https://doi.org/10.1109/IWAENC.2018.8521392 CHOPRA, Deepti – KAUR, Arvinder (2021): IoT-based Group Size Prediction and Recommendation System Using Machine Learning and Deep Learning Techniques. Discover Applied Sciences, 3(2), 1–18. Online: https://doi.org/10.1007/s42452-021-04162-x CHU, Haiyun – CHEN, Lu – YANG, Xiuxian – QIU, Xiaohui – QIAO, Zhengxue – SONG, Xuejia et al. (2021): Roles of Anxiety and Depression in Predicting Cardiovascular Disease among Patients with Type 2 Diabetes Mellitus: A Machine Learning Approach. Frontiers in Psychology, 12, 1189. Online: https://doi.org/10.3389/fpsyg.2021.645418 CHIKUSHI, Shota – KASAHARA, Jun Younes Louhi – FUJII, Hiromitsu – TAMURA, Yusuke – FARAGASSO, Angela – YAMAKAWA, Hiroshi et al. (2020): Research and Development of Construction Technology in Social Cooperation Program “Intelligent Construction System”. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction. IAARC Publications, 1536–1540. Online: https://doi.org/10.22260/ISARC2020/0213 CONG, Hongyan (2020): Personalized Recommendation of Film and Television Culture Based on an Intelligent Classification Algorithm. Personal and Ubiquitous Computing, 24(2), 165–176. Online: https://doi.org/10.1007/s00779-019-01271-8 COWEN, Alan S. – KELTNER, Dacher – SCHROFF, Florian – JO, Brendan – ADAM, Hartwig – PRASAD, Gautam (2021): Sixteen Facial Expressions Occur in Similar Contexts Worldwide. Nature, 589(7841), 251–257. Online: https://doi.org/10.1038/s41586-020-3037-7 CUI, Haoliang – SHAO, Shuai – NIU, Shaozhang – SHI, Chengjie – ZHOU, Lingyu (2021): A Classification Method for Social Information of Sellers on Social Network. EURASIP Journal on Image and Video Processing, 2021(1), 1–12. Online: https://doi.org/10.1186/s13640-020-00545-z DAM, Sumit Kumar – TURZO, Tausif Ahmed (2021): Social Movement Prediction from Bangla Social Media Data Using Gated Recurrent Unit Neural Network. In 2021 5th International Conference on Electrical Information and Communication Technology (EICT). IEEE. Online: https://doi.org/10.1109/EICT54103.2021.9733681 DANESHVAR, Hirad – RAVANMEHR, Reza (2022): A Social Hybrid Recommendation System Using LSTM and CNN. Concurrency and Computation. Practice and Experience, 34(18), e7015. Online: https://doi.org/10.1002/cpe.7015 DIAZ, Jorge Francisco Madrigal – LERASLE, Frédéric – PIBRE, Lionel – FERRANÉ, Isabelle (2021): Audio-Video Detection of the Active Speaker in Meetings. In IEEE 25th International Conference on Pattern Recognition (ICPR 2020). Online: https://hal.science/hal-03125600/file/ICPR.pdf ERTUGRUL, Ali Mert – LIN, Yu-Ru – CHUNG, Wen-Ting – YAN, Muheng – LI, Ang (2019): Activism via Attention: Interpretable Spatiotemporal Learning to Forecast Protest Activities. EPJ Data Science, 8(1), 5. Online: https://doi.org/10.1140/epjds/s13688-019-0183-y FENG, Bo – FU, Qiang – DONG, Mianxiong – GUO, Dong – LI, Qiang (2018): Multistage and Elastic Spam Detection in Mobile Social Networks through Deep Learning. IEEE Network, 32(4), 15–21. Online: https://doi.org/10.1109/MNET.2018.1700406 GALESIC, Mirta – BRUINE DE BRUIN,Wändi – DALEGE, Jonas – FELD, Scott L. – KREUTER, Frauke – OLSSON, Henrik – PRELEC, Drazen – STEIN, Daniel L. – DOES, Tamara van der (2021): Human Social Sensing is an Untapped Resource for Computational Social Science. Nature, 595(7866), 214–222. Online: https://doi.org/10.1038/s41586-021-03649-2 GAO, Yuyang – ZHAO, Liang – WU, Lingfei – YE, Yanfang – XIONG, Hui – YANG, Chaowei (2019): Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 3638–3646. Online: https://doi.org/10.1609/aaai.v33i01.33013638 Grimmer, J., Roberts, M. E., & Stewart, B. M. (2021). Machine learning for social science: An agnostic approach. Annual Review of Political Science, 24, 395-419. https://doi.org/10.1146/annurev-polisci-053119-015921 GOODFELLOW, Ian et al. (2016): Deep Learning. MIT Press. Online: www.deeplearningbook.org HANA, Karimah Mutisari – ADIWIJAYA – AL FARABY, Said – BRAMANTORO, Arif (2020): Multi-Label Classification of Indonesian Hate Speech on Twitter Using Support Vector Machines. In 2020 International Conference on Data Science and Its Applications (ICoDSA). IEEE. Online: https://doi.org/10.1109/ICoDSA50139.2020.9212992 HICKS, Diana (2004): The Four Literatures of Social Science. In MOED, Henk F. – GLÄNZEL, Wolfgang – SCHMOCH, Ulrich (eds.): Handbook of Quantitative Science and Technology Research. Dordrecht: Springer, 473–496. Online: https://doi.org/10.1007/1-4020-2755-9 HOFMAN, Jake M. – WATTS, Duncan J. – ATHEY, Susan – GARIP, Filiz – GRIFFITHS, Thomas L. – KLEINBERG, Jon – MARGETTS, Helen – MULLAINATHAN, Sendhil – SALGANIK, Matthew J. – VAZIRE, Simine – VESPIGNANI, Alessandro – YARKONI, Tal (2021): Integrating Explanation and Prediction in Computational Social Science. Nature, 595(7866), 181–188. Online: https://doi.org/10.1038/s41586-021-03659-0 IVANENKO, Alexander – WATKINS, P. – VAN GERVEN, M. A. J. – HAMMERSCHMIDT, K. – ENGLITZ, B. (2020): Classifying Sex and Strain from Mouse Ultrasonic Vocalizations Using Deep Learning. PLOS Computational Biology, 16(6), e1007918. Online: https://doi.org/10.1371/journal.pcbi.1007918 JAISWAL, Shruti – MISHRA, Pratyush – NANDI, G. C. (2018): Deep Learning Based Command Pointing Direction Estimation Using a Single RGB Camera. In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE. Online: https://doi.org/10.1109/UPCON.2018.8596762 KHAEFI, Muhammad Rizal – PRAMESTRI, Zakiya – AMIN, Imaduddin – LEE, Jong Gun (2018): Nowcasting Air Quality by Fusing Insights from Meteorological Data, Satellite Imagery and Social Media Images Using Deep Learning. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE. Online: https://doi.org/10.1109/ASONAM.2018.8508698 KHAN, Momina Rizwan – RIZVI, Shereen Zehra – YASIN, Amanullah – ALI, Mohsan (2021): Depression Analysis of Social Media Activists Using the Gated Architecture Bi-LSTM. In 2021 International Conference on Cyber Warfare and Security (ICCWS). IEEE. Online: https://doi.org/10.1109/ICCWS53234.2021.9703014 KHAN, Nida Saddaf – GHANI, Muhammad Sayeed (2021): A Survey of Deep Learning Based Models for Human Activity Recognition. Wireless Personal Communications, 120(2), 1593–1635. Online: https://doi.org/10.1007/s11277-021-08525-w KIM, Do-Hyung – LÓPEZ, Guzmán – KIEDANSKI, Diego – MADUAKO, Iyke – RÍOS, Braulio – DESCOINS, Alan – ZURUTUZA, Naroa – ARORA, Shilpa – FABIAN, Christopher (2021): Bias in Deep Neural Networks in Land Use Characterization for International Development. Remote Sensing, 13(15), 2908. Online: https://doi.org/10.3390/rs13152908 KIRA, Kenji – RENDELL, Larry A. (1992): A Practical Approach to Feature Selection. In SLEEMAN, Derek – EDWARDS, Peter (eds.): Machine Learning Proceedings 1992. Elsevier, 249–256. Online: https://doi.org/10.1016/B978-1-55860-247-2.50037-1 KUMAR, Chanchal – BHARATI, T. S. – PRAKASH, S. (2021): Online Social Network Security: A Comparative Review Using Machine Learning and Deep Learning. Neural Processing Letters, 53(1), 843–861. Online: https://doi.org/10.1007/s11063-020-10416-3 Lazer, D. M. – Pentland, A. – Watts, D. J. – Aral, S. – Athey, S. – Contractor, N., – Wagner, C. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060-1062. https://doi.org/10.1126/science.aaz817 LECUN, Yann et al. (2015): Deep Learning. Nature, 521(7553), 436–444. Online: https://doi.org/10.1038/nature14539 LI, Chang – GOLDWASSER, Dan (2021): Using Social and Linguistic Information to Adapt Pretrained Representations for Political Perspective Identification. In ZONG, Chengqing – XIA, Fei – LI, Wenjie – NAVIGLI, Roberto (eds.): Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 4569–4579. Online: https://doi.org/10.18653/v1/2021.findings-acl.401 LIU, Yajing (2021): Research on Community Public Service Information Collaborative Governance Based on Deep Learning Model. Journal of Mathematics, 2021. Online: https://doi.org/10.1155/2021/4727617 LIU, Wenhua – ZHANG, Yijie (2020): Evaluation Feedback Information for Optimization of Mental Health Courses with Deep Learning Methods. Soft Computing, 24(11), 8275–8283. Online: https://doi.org/10.1007/s00500-019-04569-0 LUDL, Dennis – GULDE, Thomas – CURIO, Christóbal (2020): Enhancing Data-Driven Algorithms for Human Pose Estimation and Action Recognition through Simulation. IEEE Transactions on Intelligent Transportation Systems, 21(9), 3990–3999. Online: https://doi.org/10.1109/TITS.2020.2988504 LUO, Ren C. – HSIEH, Chung Kai (2017): Robotic Sensory Perception on Human Mentation for Offering Proper Services. In 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE. Online: DOI: https://doi.org/10.1109/MFI.2017.8170374 MA, Qiulin – ZOU, Qi – HUANG, Yaping – WANG, Nan (2022): Dynamic Pedestrian Trajectory Forecasting with LSTM-based Delaunay Triangulation. Applied Intelligence, 52(3), 3018–3028. Online: https://doi.org/10.1007/s10489-021-02562-5 MASSON, Haemy Lasson – ISIK, Leyla (2021): Functional Selectivity for Social Interaction Perception in the Human Superior Temporal Sulcus during Natural Viewing. Neuroimage, 245, 118741. Online: https://doi.org/10.1016/j.neuroimage.2021.118741 MENG, Lingchao – WEN, Khuo-Sun – ZENG, Zhijie – BREWIN, Richard – FAN, Xiaolei – WU, Qiong (2020): The Impact of Street Space Perception Factors on Elderly Health in High-Density Cities in Macau – Analysis Based on Street View Images and Deep Learning Technology. Sustainability, 12(5), 1799. Online: https://doi.org/10.3390/su12051799 MIAO, Hang – LI, A. – YANG B. (2022): Meta-path Enhanced Lightweight Graph Neural Network for Social Recommendation. In International Conference on Database Systems for Advanced Applications. Springer, 134–149. Online: https://doi.org/10.1007/978-3-031-00126-0_9 Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence, 267, 1-38. https://doi.org/10.1016/j.artint.2018.07.007 MOON, Katie – BLACKMAN, Deborah (2014): A Guide to Understanding Social Science Research for Natural Scientists. Conservation Biology, 28(5), 1167–1177. Online: https://doi.org/10.1111/cobi.12326 NASIR, Adeel – SHAUKAT, Kamran – KHAN, Kanwal Iqbal – HAMEED, Ibrahim A. – ALAM, Talha – LUO, Suhuai (2021): Trends and Directions of Financial Technology (Fintech) in Society and Environment: A Bibliometric Study. Applied Sciences, 11(21), 10353. Online: https://doi.org/10.3390/app112110353 NI, Qingjian – ZHANG, Mei (2022): STGMN: A Gated Multi-Graph Convolutional Network Framework for Traffic Flow Prediction. Applied Intelligence, 2022, 1–14. Online: https://doi.org/10.1007/s10489-022-03224-w ÖZEROL, Gizem – SELÇUK, Semra Arslan (2022): Machine Learning in the Discipline of Architecture: A Review on the Research Trends between 2014 and 2020. International Journal of Architectural Computing, 21(1), 23–41. Online: https://doi.org/10.1177/14780771221100102 PAGE, Matthew J. – MCKENZIE, Joanne E. – BOSSUYT, Patrick M. – BOUTRON, Isabelle – HOFFMANN, Tammy C. – MULROW, Cynthia D. et al. (2021): The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. British Medical Journal, 10(1), 1–11. Online: https://doi.org/10.1136/bmj.n71 PALMER, Gregory – GREEN, Mark – BOYLAND, Emma – VASCONCELOS, Yales Stefano Rios – Savani, Rahul – Singleton, Alex (2021): A Deep Learning Approach to Identify Unhealthy Advertisements in Street View Images. Nature, 11(1), 1–12. Online: https://doi.org/10.1038/s41598-021-84572-4 POOLE, David L. – MACKWORTH, Alan K. (2010): Artificial Intelligence: Foundations of Computational Agents. New York: Cambridge University Press. RANI, Meesala Shobha – SUMATHY, Subramanain (2022): A Study on Diverse Methods and Performance Measures in Sentiment Analysis. Recent Patents on Engineering, 16(3), 12–42. Online: https://doi.org/10.2174/1872212114999201019154954 REN, Siqi – ZHOU, Yue – HE, Liming (2018): Human Trajectory Prediction with Social Information Encoding. In Pattern Recognition and Computer Vision: First Chinese Conference, PRCV 2018, Guangzhou, China, November 23–26, 262–273. Springer. Online: https://doi.org/10.1007/978-3-030-03398-9_23 SELÇUK, Ayşe Adin (2019): A Guide for Systematic Reviews: PRISMA. Turkish Archives of Otorhinolaryngology, 57(1), 57–58. Online: https://doi.org/10.5152/tao.2019.4058 SHUKLA, Priya – BHANDARI, Bhanu – GOEL, Aditya – CHARAN, Ravi – GALI, Niharika – DWIVEDI, Agam – MUNDRA, Nikhil – AGRAWAL, Ruchin – NANDI, G. C. (2019): Fusing Multimodal Human-Robot Communication using Deep Learning. In 2019 International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE. Online: https://doi.org/10.1109/UPCON47278.2019.8980213 THANG, Do Nam – NGUYEN, Lan Anh – DUNG, Pham Trung – KHOA, Truong Dang – SON, Nguyen Huu – HIEP, Nguyen Tran et al. (2018): Deep Learning-Based Multiple Objects Detection and Tracking System for Socially Aware Mobile Robot Navigation Framework. In 5th NAFOSTED Conference on Information and Computer Science (NICS). IEEE. Online: https://doi.org/10.1109/NICS.2018.8606878 TIRDAD, Kayvan – DELA CRUZ, Alex – SADEGHIAN, Alireza – CUSIMANO, Michael (2021): A Deep Neural Network Approach for Sentiment Analysis of Medically Related Texts: An Analysis of Tweets Related to Concussions in Sports. Brain Informatics, 8(1), 1–17. Online: https://doi.org/10.1186/s40708-021-00134-4 TSIKTSIRIS, Dimitris – DIMITRIOU, Nikolaos – LALAS, Antonios – DASYGENIS, Minas – VOTIS, Konstantinos – TZOVARAS, Dimitrios (2020): Real-time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures. Sensors, 20(17), 4943. Online: https://doi.org/10.3390/s20174943 XIAO, Jian – WANG, Jia – CAO, Shaozhong – LI, Bilong (2020): Application of a Novel and Improved VGG-19 Network in the Detection of Workers Wearing Masks. Journal of Physics: Conference Series, 1518. Online: https://doi.org/10.1088/1742-6596/1518/1/012041 XINGXING, Ye et al. (2021): POI Recommendation Based on Graph Enhanced Attention GNN. In 2021 11th International Conference on Intelligent Control and Information Processing (ICICIP). IEEE. Online: https://doi.org/10.1109/ICICIP53388.2021.9642167 WU, Qiong – WU, Muhong – CHEN, Xu – ZHOU, Zhi – HE, Kaiwen – CHEN, Liang (2020): DeepCP: Deep Learning Driven Cascade Prediction-Based Autonomous Content Placement in Closed Social Network. IEEE Journal on Selected Areas in Communications, 38(7), 1570–1583. Online: https://doi.org/10.1109/JSAC.2020.2999687 ZAMBONI, Simone – KEFATO, Zekarias Tilahun – GIRDZIJAUSKAS, Sarunas – NORÉN, Christoffer – DAL COL, Laura (2022): Pedestrian Trajectory Prediction with Convolutional Neural Networks. Pattern Recognition, 121, 108252. Online: https://doi.org/10.1016/j.patcog.2021.108252 ZHANG, Han – PAN, Jennifer (2019): CASM: A Deep-Learning Approach for Identifying Collective Action Events with Text and Image Data from Social Media. Sociological Methodology, 49(1), 1–57. Online: https://doi.org/10.1177/0081175019860244 ZHANG, Jie (2018): Theory and Application of Deep Neural Networks in Future Deep Space Autonomous Exploration Mission. 16th IAA Symposium On Visions and Strategies for the Future. Proceedings of the International Astronautical Congress. Online: https://iafastro.directory/iac/archive/browse/IAC-18/D4/1/47408/ ZHANG, Jiyong – LIU, Xin – ZHOU, Xiaofei (2022): Towards Non-Linear Social Recommendation Using Gaussian Process. IEEE Access, 10, 6028–6041. Online: https://doi.org/10.1109/ACCESS.2022.3141795 ZHANG, Jun – WANG, Wei – XIA, Feng – LIN, Yu-Ru – TONG, Hanghang (2020): Data-driven Computational Social Science: A Survey. Big Data Research, 21, 100145. Online: https://doi.org/10.1016/j.bdr.2020.100145 ZHANG, Yan – CHEN, Zeqiang – ZHENG, Xiang – CHEN, Nengcheng – WANG, Yongqiang (2021): Extracting the Location of Flooding Events in Urban Systems and Analyzing the Semantic Risk Using Social Sensing Data. Journal of Hydrology, 603, 127053. Online: https://doi.org/10.1016/j.jhydrol.2021.127053 ZHAO, Heng – YAP, Kim-Hui – KOT, Alex Chichung – DUAN, Lingyu (2020): Jdnet: A Joint-Learning Distilled Network for Mobile Visual Food Recognition. IEEE Journal of Selected Topics in Signal Processing, 14(4), 665–675. Online: https://doi.org/10.1109/JSTSP.2020.2969328 ZHAO, Yinou – LIU, Chi Harold (2020): Social-Aware Incentive Mechanism for Vehicular Crowdsensing by Deep Reinforcement Learning. IEEE Transactions on Intelligent Transportation Systems, 22(4), 2314–2325. Online: https://doi.org/10.1109/TITS.2020.3014263 ZHENG, Li – LIAO, Pan – LUO, Shen – SHENG, Jingwei – TENG, Pengfei – LUAN, Guoming – GAO, Jia-Hong (2019): EMS-net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes. IEEE Transactions on Medical Imaging, 39(6), 1833–1844. Online: https://doi.org/10.1109/TMI.2019.2958699 ZHOU, Hao – HE, Shenjing – CAI, Yuyang – WANG, Miao – SU, Shiliang (2019): Social Inequalities in Neighborhood Visual Walkability: Using Street View Imagery and Deep Learning Technologies to Facilitate Healthy City Planning. Sustainable Cities and Society, 50, 101605. Online: https://doi.org/10.1016/j.scs.2019.101605" ["copyrightYear"]=> int(2024) ["issueId"]=> int(594) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(4) "9-51" ["pub-id::doi"]=> string(21) "10.32575/ppb.2024.1.2" ["abstract"]=> array(2) { ["en_US"]=> string(1271) "

Artificial intelligence (AI) is widely used in social sciences and continues to evolve. Deep learning (DL) has emerged as a powerful AI tool transforming the social sciences with valuable insights across many areas. Employing DL for modelling social sciences’ big data has led to significant discoveries and transformations. This study aims to systematically review and evaluate DL methods in the social sciences. Following PRISMA guideline, this study identifies fundamental DL methods applied to social science applications. We evaluated DL models using reported metrics and calculated a normalised reliability score for uniform assessment. Employing relief feature selection, we identified influential parameters affecting DL techniques’ reliability. Findings suggest that evaluation criteria significantly impact DL model effectiveness, while database and application type influence moderately. Identified limitations include inadequate reporting of evaluation criteria and model structure details hindering comprehensive assessment and informed policy development. In conclusion, this review underscores DL methods’ transformative role in the social sciences, emphasising the importance of explainability and responsibility.

" ["hu_HU"]=> string(2182) "

Deep learning (DL) has emerged as a cutting-edge data-driven methodology, revolutionizing the field of social sciences by providing profound insights. The application of DL techniques to model social science big data has resulted in significant discoveries and a rapid transformation of traditional methodologies. In this study, we aim to systematically review and evaluate the performance of DL methods in the social sciences. To ensure a rigorous and efficient exploration of relevant databases, we adhere to the PRISMA guidelines. Publications were sourced from Scopus and Web of Science (WoS). The search syntax encompassed essential DL methods, such as convolutional neural network (CNN), Long short-term memory (LSTM), deep neural network (DNN), deep belief network (DBN), Recurrent neural networks (RNN), and deep reinforcement learning (DRL), specifically applied to the social sciences. We utilized a comprehensive search filter to focus on the DL section and its various applications in the social sciences. These applications were categorized into twelve domains, including social information, social network analysis, social development, social movements, social inequalities, social cooperation, social conflict, social technology, social health, social risk, social environment, and social media. To evaluate the performance of DL models, we analyzed the evaluation metrics reported in each study. A normalized reliability score was calculated to facilitate a uniform evaluation of models across different applications. Furthermore, we employed a relief feature selection technique to identify the most influential parameter affecting the reliability score of DL techniques in social science applications. Our findings suggest that evaluation criteria play a crucial role in determining the effectiveness of DL models, while the influence of the database and application type is moderate. However, certain limitations were identified within the studies reviewed. One prominent limitation is the lack of reporting evaluation criteria values during the evaluation phase, which hinders a comprehensive assessment of the employed models...

" } ["copyrightHolder"]=> array(1) { ["en_US"]=> string(56) "Amir Mosavi, Sina Ardabili, Makó Csaba, Sasvári Péter" } ["title"]=> array(2) { ["en_US"]=> string(81) "A Comprehensive Review and Evaluation of Deep Learning Methods in Social Sciences" ["hu_HU"]=> string(81) "A Comprehensive Review and Evaluation of Deep Learning Methods in Social Sciences" } ["locale"]=> string(5) "en_US" ["authors"]=> array(4) { [0]=> object(Author)#814 (6) { ["_data"]=> array(15) { ["id"]=> int(9664) ["email"]=> string(19) "s.ardabili@ieee.org" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(7056) ["seq"]=> int(1) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(4) "Sina" ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(8) "Ardabili" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [1]=> object(Author)#850 (6) { ["_data"]=> array(15) { ["id"]=> int(8750) ["email"]=> string(28) "Mosavi.Amirhosein@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(7056) ["seq"]=> int(1) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(4) "Amir" ["hu_HU"]=> string(6) "Mosavi" } ["givenName"]=> array(2) { ["en_US"]=> string(6) "Mosavi" ["hu_HU"]=> string(4) "Amir" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [2]=> object(Author)#849 (6) { ["_data"]=> array(15) { ["id"]=> int(9665) ["email"]=> string(21) "mako.csaba@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(7056) ["seq"]=> int(1) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(5) "Makó" ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(5) "Csaba" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [3]=> object(Author)#844 (6) { ["_data"]=> array(15) { ["id"]=> int(9666) ["email"]=> string(24) "sasvari.peter@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(7056) ["seq"]=> int(1) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(8) "Sasvári" ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(6) "Péter" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(2) { ["hu_HU"]=> array(1) { [0]=> string(64) "Social science; deep learning; big data; artificial intelligence" } ["en_US"]=> array(6) { [0]=> string(14) "social science" [1]=> string(13) "deep learning" [2]=> string(8) "big data" [3]=> string(16) "machine learning" [4]=> string(23) "artificial intelligence" [5]=> string(34) "generative artificial intelligence" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#820 (7) { ["_submissionFile"]=> NULL ["_data"]=> array(9) { ["submissionFileId"]=> int(34645) ["id"]=> int(5946) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(7056) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }
PDF

Machine Learning in Smart Grids A Systematic Review, Novel Taxonomy, and Comparative Performance Evaluation

Rituraj Rituraj, Várkonyi T. Dániel, Amir Mosavi, Pap József, Várkonyi-Kóczy R. Annamária, Makó Csaba
doi: 10.32575/ppb.2024.1.3
53-83.
object(Publication)#147 (6) { ["_data"]=> array(29) { ["id"]=> int(6999) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2024-06-28" ["lastModified"]=> string(19) "2024-07-17 10:30:34" ["primaryContactId"]=> int(9667) ["sectionId"]=> int(17) ["seq"]=> int(2) ["submissionId"]=> int(6875) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(27297) "ACHLERKAR, Pankaj D. – SAMANTARAY, S. R. – MANIKANDAN, M. Sabbarimalai (2016): Variational Mode Decomposition and Decision Tree-based Detection and Classification of Power Quality Disturbances in Grid-Connected Distributed Generation System. IEEE Transactions on Smart Grid, 9(4), 3122–3132. Online: https://doi.org/10.1109/TSG.2016.2626469 Ahmad, T. – Zhu, H., Zhang, D. – Tariq, R., Bassam, A. – Ullah, F., – Alshamrani, S. S. (2022): Energetics Systems and artificial intelligence: Applications of industry 4.0. Energy Reports, 8, 334-361. Online: https://doi.org/10.1016/j.egyr.2021.11.256 AHMED, Saeed – LEE, Youngdoo – HYUN, Seung-Ho – KOO, Insoo (2019): Unsupervised Machine Learning-Based Detection of Covert Data Integrity Assault in Smart Grid Networks Utilizing Isolation Forest. IEEE Transactions on Information Forensics and Security, 14(10), 2765–2777. Online: https://doi.org/10.1109/TIFS.2019.2902822 ARDITO, Luca – PROCACCIANTI, Giuseppe – MENGA, Giuseppe – MORISIO, Maurizio (2013): Smart Grid Technologies in Europe: An Overview. Energies, 6(1), 251–281. Online: https://doi.org/10.3390/en6010251 AREO, Oluwafadekemi S. – GERSHON, Obindah – OSABUOHIEN, Evans (2020): Improved Public Services and Tax Compliance of Small and Medium Scale Enterprises in Nigeria: A Generalised Ordered Logistic Regression. Asian Economic and Financial Review, 10(7), 833–860. Online: https://doi.org/10.18488/journal.aefr.2020.107.833.860 ALY, Hamed H.H. (2020): A Proposed Intelligent Short-Term Load Forecasting Hybrid Models of ANN, WNN and KF Based on Clustering Techniques for Smart Grid. Electric Power Systems Research, 182, 106191. Online: https://doi.org/10.1016/j.epsr.2019.106191 BANGA, Alisha – AHUJA, Ravinder – SHARMA, S. C. (2022): Accurate Detection of Electricity Theft Using Classification Algorithms and Internet of Things in Smart Grid. Arabian Journal for Science and Engineering, 47(8), 9583–9599. Online: https://doi.org/10.1007/s13369-021-06313-z BAND, Shahab S. – ARDABILI, Sina – SOOKHAK, Mehdi – CHRONOPOULOS, Theodore A. – ELNAFFAR, Said – MOSLEHPOUR, Massoud – Mako, Csaba – Török Bernát – Pai, Hao-Ting –MOSAVI, Amir (2022): When Smart Cities Get Smarter via Machine Learning: An In-depth Literature Review. IEEE Access, 10, 60985–61015. Online: https://doi.org/10.1109/ACCESS.2022.3181718 BAO, Man – ZHANG, Hongqi – WU, Hao – ZHANG, Chao – WANG, Zixu – ZHANG, Xiaohui (2022): Multiobjective Optimal Dispatching of Smart Grid Based on PSO and SVM. Mobile Information Systems, 2051773. Online: https://doi.org/10.1155/2022/2051773 BASHIR, Ali K. – KHAN, Suleman – PRABADEVI, B. – DEEPA, N. – ALNUMAY, Waleed S. – GADEKALLU, Thippa R. – MADDIKUNTA, Praveen K. R. (2021): Comparative Analysis of Machine Learning Algorithms for Prediction of Smart Grid Stability†. International Transactions on Electrical Energy Systems, 31(9), e12706. Online: https://doi.org/10.1002/2050-7038.12706 BARI, Ataul – JIANG, Jin – SAAD, Walid – JAEKEL, Arunita (2014): Challenges in the Smart Grid Applications: An Overview. International Journal of Distributed Sensor Networks, 10(2), 974682. Online: https://doi.org/10.1155/2014/974682 BASHIR, Hayat – LEE, Seonah – KIM, Kyong H. (2022): Resource Allocation through Logistic Regression and Multicriteria Decision Making Method in IoT Fog Computing. Transactions on Emerging Telecommunications Technologies, 33(2), e3824. Online: https://doi.org/10.1002/ett.3824 BEHARA, Ramesh K. – SAHA, Akshay K. (2022): Artificial Intelligence Methodologies in Smart Grid-Integrated Doubly Fed Induction Generator Design Optimization and Reliability Assessment: A Review. Energies, 15(19), 7164. Online: https://doi.org/10.3390/en15197164 BERGHOUT, Tarek – BENBOUZID, Mohamed – MUYEEN, S. M. (2022): Machine Learning for Cybersecurity in Smart Grids: A Comprehensive Review-based Study on Methods, Solutions, and Prospects. International Journal of Critical Infrastructure Protection, 38, 100547. Online: https://doi.org/10.1016/j.ijcip.2022.100547 BHAGYA RAJ, G. V. S. – DASH, Kshirod K. (2022): Comprehensive Study on Applications of Artificial Neural Network in Food Process Modeling. Critical Reviews in Food Science and Nutrition, 62(10), 2756–2783. Online: https://doi.org/10.1080/10408398.2020.1858398 BLANCO, Victor – JAPÓN, Alberto – PUERTO, Justo (2022): A Mathematical Programming Approach to SVM-based Classification with Label Noise. Computers & Industrial Engineering, 172, 108611. Online: https://doi.org/10.1016/j.cie.2022.108611 CHAURASIA, Kiran – KAMATH, H Ravishankar (2022): Artificial Intelligence and Machine Learning Based: Advances in Demand-Side Response of Renewable Energy-Integrated Smart Grid. In SOMANI, Arun K. – MUNDRA, Ankit – DOSS, Robin – BHATTACHARYA, Subhajit (eds.): Smart Systems: Innovations in Computing. Singapore: Springer, 195–207. Online: https://doi.org/10.1007/978-981-16-2877-1_18 CHEN, Lingxuan – XIE, Pengyang – MA, Shuaihua – YANG, Chen (2021): Evaluation of Low-Carbon Benefits of Smart Grid Based on Random Forest Algorithm. IOP Conference Series: Earth and Environmental Science, 804(3), 032001. Online: https://doi.org/10.1088/1755-1315/804/3/032001 CHEN, Zhenyu – YUAN, Shuai – WU, Longfei – GUAN, Zhitao – DU, Xiaojiang (2022): False Data Injection Attack Detection Based on Wavelet Packet Decomposition and Random Forest in Smart Grid. In 2021 IEEE 23rd International Conference on High Performance Computing and Communications, 7th International Conference on Data Science and Systems, 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys Haikou, Hainan, China, 1965–1971. Online: https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00294 CUI, Lei – QU, Youyang – GAO, Longxiang – XIE, Gang – YU, Shui (2020): Detecting False Data Attacks Using Machine Learning Techniques in Smart Grid: A Survey. Journal of Network and Computer Applications, 170, 102808. Online: https://doi.org/10.1016/j.jnca.2020.102808 DA CUNHA, Guilherme L. – FERNANDES, Ricardo A. S. – FERNANDES, Tatiane C. C. (2022): Small-Signal Stability Analysis In Smart Grids: An Approach Based on Distributed Decision Trees. Electric Power Systems Research, 203, 107651. Online: https://doi.org/10.1016/j.epsr.2021.107651 DAS, Ratnakar – MISHRA, Jibitesh – MISHRA, Sujogya – PATTNAIK, P. K. – DAS, Subhalaxmi (2022): Mathematical Modeling using Rough Set and Random Forest Model to Predict Wind Speed. In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 207–213. Online: https://doi.org/10.23919/INDIACom54597.2022.9763275 DENG, Shuang – WEI, Minghui – XU, Mingze – CAI, Wei (2021): Prediction of the Rate of Penetration Using Logistic Regression Algorithm of Machine Learning Model. Arabian Journal of Geosciences, 14(21), 1–13. Online: https://doi.org/10.1007/s12517-021-08452-x DEWANGAN, Fanidhar – BISWAL, Monalisa – PATNAIK, Bhaskar – HASAN, Shazia – MISHRA, Manohar (2022): Smart Grid Stability Prediction Using Genetic Algorithm-Based Extreme Learning Machine. In BANSAL, Ramesh C. – MISHRA, Manohar – SOOD, Yog Raj (eds.): Electric Power Systems Resiliency: Modelling, Opportunity and Challenges. [s. l.]: Academic Press, 149–163. Online: https://doi.org/10.1016/B978-0-323-85536-5.00011-4 DOU, Chunxia – WU, Di – YUE, Dong – JIN, Bao – XU, Shiyun (2019): A Hybrid Method for False Data Injection Attack Detection in Smart Grid Based on Variational Mode Decomposition and OS-ELM. CSEE Journal of Power and Energy Systems, PP (99), 118195762. Online: https://doi.org/10.17775/CSEEJPES.2019.00670 DOU, Chunxia – WU, Di – YUE, Dong – JIN, Bao – XU, Shiyun (2022): A Hybrid Method for False Data Injection Attack Detection in Smart Grid Based on Variational Mode Decomposition and OS-ELM. CSEE Journal of Power and Energy Systems, 8(6), 1697–1707. Online: https://doi.org/10.17775/CSEEJPES.2019.00670 DURAIRAJ, Danalakshmi – WRÓBLEWSKI, Łukasz – SHEELA, A. – HARIHARASUDAN, A. – URBAŃSKI, Mariusz (2022): Random Forest Based Power Sustainability and Cost Optimization in Smart Grid. Production Engineering Archives, 28(1), 82–92. Online: https://doi.org/10.30657/pea.2022.28.10 EISSA, M. M. – ALI, A. A. – ABDEL-LATIF, K. M. – AL-KADY, A. F. (2019): A Frequency Control Technique Based on Decision Tree Concept by Managing Thermostatically Controllable Loads at Smart Grids. International Journal of Electrical Power and Energy Systems, 108, 40–51. Online: https://doi.org/10.1016/j.ijepes.2018.12.037 ELBOUCHIKHI, Elhoussin – ZIA, Muhammad F. – BENBOUZID, Mohamed – EL HANI, Soumia (2021): Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring. Electronics, 10(21), 2725. Online: https://doi.org/10.3390/electronics10212725 GANESAN, Anusha – PAUL, Anand – SEO, HyunCheol (2022): Elderly People Activity Recognition in Smart Grid Monitoring Environment Mathematical Problems in Engineering, 2022, art. no. 9540033 Online: https://doi.org/10.1155/2022/9540033 GUPTA, Praveen K. – SINGH, Neeraj K. – MAHAJAN, Vasundhara (2021): Intrusion Detection in Cyber-Physical Layer of Smart Grid Using Intelligent Loop Based Artificial Neural Network Technique. International Journal of Engineering, Transactions B: Applications, 34(5), 1250–1256. Online: https://doi.org/10.5829/ije.2021.34.05b.18 GUYOT, Dimitri – GIRAUD, Florine – SIMON, Florian – CORGIER, David – MARVILLET, Christophe – TREMEAC, Brice (2019): Overview of the Use of Artificial Neural Networks for Energy‐Related Applications in the Building Sector. International Journal of Energy Research, 43(13), 6680–6720. Online: https://doi.org/10.1002/er.4706 HAFEEZ, Ghulam – ALIMGEER, Khurram S. – QAZI, Abdul B. – KHAN, Imran – USMAN, Muhammad – KHAN, Farrukh A. – WADUD, Zahid (2020): A Hybrid Approach for Energy Consumption Forecasting with a New Feature Engineering and Optimization Framework in Smart Grid. IEEE Access, 8, 9057473. 96210–96226. Online: https://doi.org/10.1109/ACCESS.2020.2985732 HEIDARI, M. – SEIFOSSADAT, G. – RAZAZ, M. (2013): Application of Decision Tree and Discrete Wavelet Transform for an Optimized Intelligent-Based Islanding Detection Method in Distributed Systems with Distributed Generations. Renewable and Sustainable Energy Reviews, 27, 525–532. Online: https://doi.org/10.1016/j.rser.2013.06.047 HEWETT, Timothy E. – WEBSTER, Kate E. – HURD, Wendy J. (2019): Systematic Selection of Key Logistic Regression Variables for Risk Prediction Analyses: A Five Factor Maximum Model. Clinical Journal of Sport Medicine, 29(1), 78–85. Online: https://doi.org/10.1097/JSM.0000000000000486 HOSSAIN, Eklas – KHAN, Imtiaj – UN-NOOR, Fuad – SIKANDER, Sarder S – SUNNY, M. Samiul (2019): Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review. IEEE Access, 7, 13960–13988. Online: https://doi.org/10.1109/ACCESS.2019.2894819 HUA, Weiqi – CHEN, Ying – QADRDAN, Meysam – JIANG, Jing – SUN, Hongjian – WU, Jianzhong (2022): Applications of Blockchain and Artificial Intelligence Technologies for Enabling Prosumers in Smart Grids: A Review. Renewable and Sustainable Energy Reviews, 161, 112308. Online: https://doi.org/10.1016/j.rser.2022.112308 JARMOUNI, Eziitouni – MOUHSEN, Ahmed – LAMHAMMEDI, Mohammed – OULDZIRA, Hicham (2021): Integration of Artificial Neural Networks for Multi-Source Energy Management in a Smart Grid. International Journal of Power Electronics and Drive Systems, 12(3), 1919–1927. Online: https://doi.org/10.11591/ijpeds.v12.i3.pp1919-1927 JAVAID, Nadeem – QASIM, Umar – YAHAYA, Adamu S. – ALKHAMMASH, Eman H. – HADJOUNI, Myriam (2022): Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids. IEEE Access, 10, 56863–56875. Online: https://doi.org/10.1109/ACCESS.2022.3171229 JAWAD, Jasir – HAWARI, Alaa H. – ZAIDI, Syed J. (2021): Artificial Neural Network Modeling of Wastewater Treatment and Desalination Using Membrane Processes: A Review. Chemical Engineering Journal, 419, 129540. Online: https://doi.org/10.1016/j.cej.2021.129540 Jena, M. – Dehuri, S. (2020). An Experimental Study on Decision Tree Classifier Using Discrete and Continuous Data. In: Mallick, P., Balas, V., Bhoi, A., Chae, GS. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 1040. Springer, Singapore. Online: https://doi.org/10.1007/978-981-15-1451-7_35 KARIMINIA, Shahab – SHAMSHIRBAND, Shahaboddin – MOTAMEDI, Shervin – HASHIM, Roslan – ROY, Chandrabhushan (2016): A Systematic Extreme Learning Machine Approach to Analyze Visitors’ Thermal Comfort at a Public Urban Space. Renewable and Sustainable Energy Reviews, 58, 751–760. Online: https://doi.org/10.1016/j.rser.2015.12.321 KWAK, Moon K. – HEO, Seok (2007): Active Vibration Control of Smart Grid Structure by Multi-input and Multioutput Positive Position Feedback Controller. Journal of Sound and Vibration, 304(1–2), 230–245. Online: https://doi.org/10.1016/j.jsv.2007.02.021 LI, B. – DELPHA, C. – DIALLO, D. – MIGAN-DUBOIS, A. (2021): Application of Artificial Neural Networks to Photovoltaic Fault Detection and Diagnosis: A Review. Renewable and Sustainable Energy Reviews, 138, 110512. Online: https://doi.org/10.1016/j.rser.2020.110512 LI, Huifang – LI, Yidong – DONG, Hairong (2017): A Comprehensive Learning-Based Model for Power Load Forecasting in Smart Grid. Computing and Informatics, 36(2), 470–492. Online: https://doi.org/10.4149/cai_2017_2_470 LI, Xiaonuo – YI, Shiyi – CUNDY, Andrew B. – CHEN, Weiping (2022): Sustainable Decision-Making for Contaminated Site Risk Management: A Decision Tree Model Using Machine Learning Algorithms. Journal of Cleaner Production, 371, 133612. Online: https://doi.org/10.1016/j.jclepro.2022.133612 LI, Yuancheng – QIU, Rixuan – JING, Sitong (2018): Intrusion Detection System using Online Sequence Extreme Learning Machine (OSELM) in Advanced Metering Infrastructure of Smart Grid. PLoS ONE, 13(2), e0192216. Online: https://doi.org/10.1371/journal.pone.0192216 LIN, Rongheng – PEI, Zixiang – YE, Zezhou – WU, Budan – YANG, Geng (2020): A Voted Based Random Forests Algorithm for Smart Grid Distribution Network Faults Prediction. Enterprise Information Systems, 14(4), 496–514. Online: https://doi.org/10.1080/17517575.2019.1600724 LIU, Fen – YU, Zheng – WANG, Yixi – FENG, Hao – ZHA, Zhiyong – LIAO, Rongtao – ZHANG, Ying (2020): Falsified Data Filtering Method for Smart Grid Wireless Communication Based on SVM. International Journal of Internet Protocol Technology, 13(4), 177–183. Online: https://doi.org/10.1504/IJIPT.2020.10028657 LIU, Tian – SHU, Tao (2021): On the Security of ANN-Based AC State Estimation in Smart Grid. Computers and Security, 105, 102265. Online: https://doi.org/10.1016/j.cose.2021.102265 LIU, Xinchun (2021): Empirical Analysis of Financial Statement Fraud of Listed Companies Based on Logistic Regression and Random Forest Algorithm. Journal of Mathematics, 2021(Special Issue). Online: https://doi.org/10.1155/2021/9241338 MA, Suliang – CHEN, Mingxuan – WU, Jianwen – WANG, Yuhao – JIA, Bowen – JIANG, Yuan (2019): High-Voltage Circuit Breaker Fault Diagnosis Using a Hybrid Feature Transformation Approach Based on Random Forest and Stacked Autoencoder. IEEE Transactions on Industrial Electronics, 66(12), 9777–9788. Online: https://doi.org/10.1109/TIE.2018.2879308 MANOHARAN, Hariprasath – TEEKARAMAN, Yuvaraja – KIRPICHNIKOVA, Irina – KUPPUSAMY, Ramya – NIKOLOVSKI, Srete – BAGHAEE, Hamid R. (2020): Smart Grid Monitoring by Wireless Sensors Using Binary Logistic Regression. Energies, 13(15), 3974. Online: https://doi.org/10.3390/en13153974 MEI, ShengWei – CHEN, LaiJun (2013): Recent Advances on Smart Grid Technology and Renewable Energy Integration. Science China Technological Sciences, 56(12), 3040–3048. Online: https://doi.org/10.1007/s11431-013-5414-z MISHRA, Manohar – NAYAK, Janmenjoy – NAIK, Bignaraj – PATNAIK, Bhaskar (2022): Enhanced Memetic Algorithm-Based Extreme Learning Machine Model for Smart Grid Stability Prediction. International Transactions on Electrical Energy Systems, 2022. Online: https://doi.org/10.1155/2022/8038753 MOLDOVAN, Dorin (2021): Improved Kangaroo Mob Optimization and Logistic Regression for Smart Grid Stability Classification. In SILHAVY, R. (ed.): Artificial Intelligence in Intelligent Systems. CSOC 2021. Lecture Notes in Networks and Systems, vol 229. Cham. Springer, 469–487. Online: https://doi.org/10.1007/978-3-030-77445-5_44 MUKHERJEE, Amartya – MUKHERJEE, Prateeti – DEY, Nilanjan – DE, Debashis – PANIGRAHI, B. K. (2020): Lightweight Sustainable Intelligent Load Forecasting Platform for Smart Grid Applications. Sustainable Computing: Informatics and Systems, 25, 100356. Online: https://doi.org/10.1016/j.suscom.2019.100356 MOHANTY, D. K. – PARIDA, Ajaya K. – KHUNTIA, Shelly S. (2021): Financial Market Prediction under Deep Learning Framework using Auto Encoder and Kernel Extreme Learning Machine. Applied Soft Computing, 99, 106898. Online: https://doi.org/10.1016/j.asoc.2020.106898 NAYAK, Janmenjoy – VAKULA, Kanithi – DINESH, Paidi – NAIK, Bighnaraj – PELUSI, Danilo (2020): Intelligent Food Processing: Journey from Artificial Neural Network to Deep Learning. Computer Science Review, 38, 100297. Online: https://doi.org/10.1016/j.cosrev.2020.100297 NAZ, Aqdas – JAVED, Muhammad U. – JAVAID, Nadeem – SABA, Tanzila – ALHUSSEIN, Musaed – AURANGZEB, Khursheed (2019): Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids. Energies, 12(5), 866. Online: https://doi.org/10.3390/en12050866 NIAZI, K. R. – ARORA, C. M. – SURANA, S. L. (1999): Transient Security Evaluation of Power Systems Using Decision Tree Method. Journal-Institution of Engineers India Part El Electrical Engineering Division, 76–79. OMITAOMU, Olufemi A. – NIU, Haoran (2021): Artificial Intelligence Techniques in Smart Grid: A Survey. Smart Cities, 4(2), 548–568. Online: https://doi.org/10.3390/smartcities4020029 RADOGLOU-GRAMMATIKIS, Panagiotis I. – SARIGIANNIDIS, Panagiotis G. (2019): An Anomaly-Based Intrusion Detection System for the Smart Grid Based on CART Decision Tree. 2018 Global Information Infrastructure and Networking Symposium (GIIS) art. no. 8635743 Online: https://doi.org/10.1109/GIIS.2018.8635743 RANGANATHAN, Raghuram – QIU, Robert – HU, Zhen – HOU, Shujie – PAZOS-REVILLA, Marbin – ZHENG, Gang – ZHE, Chen – GUO, Nan (2011): Cognitive Radio for Smart Grid: Theory, Algorithms, and Security. International Journal of Digital Multimedia Broadcasting, 2011. Online: https://doi.org/10.1155/2011/502087 RANGEL-MARTINEZ, Daniel – NIGAM, K. D. – RICARDEZ-SANDOVAL, Luis A. (2021): Machine Learning on Sustainable Energy: A Review and Outlook on Renewable Energy Systems, Catalysis, Smart Grid and Energy Storage. Chemical Engineering Research and Design, 174, 414–441. Online: https://doi.org/10.1016/j.cherd.2021.08.013 RAZA, Muhammad Q. – KHOSRAVI, Abbas (2015): A Review on Artificial Intelligence-based Load Demand Forecasting Techniques for Smart Grid and Buildings. Renewable and Sustainable Energy Reviews, 50, 1352–1372. Online: https://doi.org/10.1016/j.rser.2015.04.065 ROGER, Coleen – LASBLEIZ, Adèle – GUYE, Maxime – DUTOUR, Anne – GABORIT, Bénédicte – RANJEVA, Jean-Philippe (2022): The Role of the Human Hypothalamus in Food Intake Networks: An MRI Perspective. Frontiers in Nutrition, 8, 1191. Online: https://doi.org/10.3389/fnut.2021.760914 Singh, V.K., Govindarasu, M. (2021): Cyber Kill Chain-Based Hybrid Intrusion Detection System for Smart Grid. In: Haes Alhelou, H., Abdelaziz, A.Y., Siano, P. (eds) Wide Area Power Systems Stability, Protection, and Security. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-54275-7_22 STEER, Kent C. B. – WIRTH, Andrew – HALGAMUGE, Saman K. (2012): Decision Tree Ensembles for Online Operation of Large Smart Grids. Energy Conversion and Management, 59, 9–18. Online: https://doi.org/10.1016/j.enconman.2012.01.010 SUN, Li-Yan – MIAO, Cheng-lin – YANG, Li (2018): Ecological Environmental Early-Warning Model for Strategic Emerging Industries in China Based on Logistic Regression. Ecological Indicators, 84, 748–752. Online: https://doi.org/10.1016/j.ecolind.2017.09.036 TAGHAVINEJAD, Seyedeh M. – TAGHAVINEJAD, Mehran – SHAHMIRI, Lida – ZAVVAR, M. – ZAVVAR, Mohammad H. (2020): Intrusion Detection in IoT-Based Smart Grid Using Hybrid Decision Tree. 2020 6th International Conference on Web Research (ICWR), Tehran, Iran. 152–156. Online: https://doi.org/10.1109/ICWR49608.2020.9122320 TAHIR, Aroosa – KHAN, Zahoor A. – JAVAID, Nadeem – HUSSAIN, Zeeshan – RASOOL, Aimen – AIMAL, Syeda (2019): Load and Price Forecasting Based on Enhanced Logistic Regression in Smart Grid. In BAROLLI, L. – XHAFA, F. – KHAN, Z. – ODHABI, H. (eds.): Advances in Internet, Data and Web Technologies. EIDWT 2019. Cham: Springer, 221–233. Online: https://doi.org/10.1007/978-3-030-12839-5_21 TEEKARAMAN, Yuvaraja – KIRPICHNIKOVA, Irina – MANOHARAN, Hariprasath – KUPPUSAMY, Ramya – ANGADI, Ravi V. – THELKAR, Amruth R. (2022): Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems. Wireless Communications and Mobile Computing, 6942029. Online: https://doi.org/10.1155/2022/6942029 TEHRANI, Soroush O. – MOGHADDAM, Mohammad H. Y. – ASADI, Mohsen (2020): Decision Tree based Electricity Theft Detection in Smart Grid. Proceedings of the 4th International Conference on Smart Cities, Internet of Things and Applications (SCIoT), Mashad, Iran, 46–51. Online: https://doi.org/10.1109/SCIOT50840.2020.9250194 TOMAR, Divya – AGARWAL, Sonali (2015): Twin Support Vector Machine: A Review from 2007 to 2014. Egyptian Informatics Journal, 16(1), 55–69. Online: https://doi.org/10.1016/j.eij.2014.12.003 TURANZAS, J. – ALONSO, M. – AMARIS, H. – GUTIERREZ, J. – PASTRANA, S. (2022): A Nested Decision Tree for Event Detection in Smart Grids. Renewable Energy and Power Quality Journal, 20, 353–358. Online: https://doi.org/10.24084/repqj20.308 TYRALIS, Hristos – PAPACHARALAMPOUS, Georgia – LANGOUSIS, Andreas (2019): A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources. Water, 11(5), 910. Online: https://doi.org/10.3390/w11050910 VENKATASUBRAMANIAM, Ashwini – WOLFSON, Julian – MITCHELL, Nathan – BARNES, Timothy – JAKA, Meghan – FRENCH, Simone (2017): Decision Trees in Epidemiological Research. Emerging Themes in Epidemiology, 14(11), 1–12. Online: https://doi.org/10.1186/s12982-017-0064-4 WANG, N. – TIAN, J.-Y. – DONG, N. – HAN, M. – CHEN, Y. (2022): Real-Time Risk-Assessment and Early-Warning Technology of Smart Grid Regulation System Based on Improved SVM. Journal of Shenyang University of Technology, 44(1), 7–13. Online: https://xb.sut.edu.cn/EN/10.7688/j.issn.1000-1646.2022.01.02 WANG, Yuanni – KONG, Tao (2019): Air Quality Predictive Modeling Based on an Improved Decision Tree in a Weather-Smart Grid. IEEE Access, 7, 8917641. 172892–172901. Online: https://doi.org/10.1109/ACCESS.2019.2956599 XU, Chongchong – LIAO, Zhicheng – LI, Chaojie – ZHOU, Xiaojun – XIE, Renyou (2022): Review on Interpretable Machine Learning in Smart Grid. Energies, 15(12), 4427. Online: https://doi.org/10.3390/en15124427 XU, Nuo – DENG, Fan – LIU, Bingqi – LI, Caixia – FU, Hancong – YANG, Huan – ZHANG, Jiahua (2021): Changes in the Urban Surface Thermal Environment of a Chinese Coastal City Revealed by Downscaling MODIS LST with Random Forest Algorithm. Journal of Meteorological Research, 35(5), 759–774. Online: https://doi.org/10.1007/s13351-021-0023-4 XUE, Dongbo – JING, Xiaorong – LIU, Hongqing (2019): Detection of False Data Injection Attacks in Smart Grid Utilizing ELM-based OCON Framework. IEEE Access, 7, 31762–31773. Online: https://doi.org/10.1109/ACCESS.2019.2902910 YANG, Liqun – LI, Yuancheng – LI, Zhoujun (2017): Improved-ELM Method for Detecting False Data Attack in Smart Grid. International Journal of Electrical Power and Energy Systems, 91, 183–191. Online: https://doi.org/10.1016/j.ijepes.2017.03.011 YASEEN, Zaher M. – SULAIMAN, Sadeq O. – DEO, Ravinesh C. – CHAU, Kwok-Wing (2019): An Enhanced Extreme Learning Machine Model for River Flow Forecasting: State-of-the-art, Practical Applications in Water Resource Engineering Area and Future Research Direction. Journal of Hydrology, 569, 387–408. Online: https://doi.org/10.1016/j.jhydrol.2018.11.069 ZAIN, Azlan M. – HARON, Habibollah – QASEM, Sultan N. – SHARIF, Safian (2012): Regression and ANN Models for Estimating Minimum Value of Machining Performance. Applied Mathematical Modelling, 36(4), 1477–1492. Online: https://doi.org/10.1016/j.apm.2011.09.035 Zekić-Sušac, M., Has, A., & Knežević, M. (2021). Predicting energy cost of public buildings by artificial neural networks, CART, and random forest. Neurocomputing, 439, 223-233. Online: https://doi.org/10.1016/j.neucom.2020.01.124 ZHANG, Ke – HU, Zhi – ZHAN, Yufei – WANG, Xiaofen – GUO, Keyi (2020): A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine. Energies, 13(18), 4907. Online: https://doi.org/10.3390/en13184907 ZHANG, Yanyu – ZENG, Peng – ZANG, Chuanzhi (2014): Review of Home Energy Management System in Smart Grid. Power System Protection and Control, 42(18), 144–154. ZHENG, Xiulin – LI, Peipei – WU, Xindong (2022): Data Stream Classification Based on Extreme Learning Machine: A Review. Big Data Research, 30, 100356. Online: https://doi.org/10.1016/j.bdr.2022.100356 ZHENG, Yingying – CELIK, Berk – SURYANARAYANAN, Siddharth – MACIEJEWSKI, Anthony A. – SIEGEL, Howard J. – HANSEN, Timothy M. (2021): An Aggregator-Based Resource Allocation in the Smart Grid using an Artificial Neural Network and Sliding Time Window Optimization. IET Smart Grid, 4(6), 612–622. Online: https://doi.org/10.1049/stg2.12042 ZIDI, Salah – MIHOUB, Alaeddin– MIAN QAISAR, Saeed – KRICHEN, Moez – ABU AL-HAIJA, Qasem (2022): Theft Detection Dataset for Benchmarking and Machine Learning Based Classification in a Smart Grid Environment. Journal of King Saud University – Computer and Information Sciences, 35(1), 13–25. Online: https://doi.org/10.1016/j.jksuci.2022.05.007 ZUBIA, I. – ARRAMBIDE, I. – AZURZA, O. – GARCÍA, P. M. – UGARTEMENDIA, J. J. (2013): Rural Smart Grids: Planning, Operation and Control Review. Renewable Energy and Power Quality Journal, 1(11), 1099–1104. Online: https://doi.org/10.24084/repqj11.544" ["copyrightYear"]=> int(2024) ["issueId"]=> int(594) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(5) "53-83" ["pub-id::doi"]=> string(21) "10.32575/ppb.2024.1.3" ["abstract"]=> array(2) { ["en_US"]=> string(963) "

This article presents a state-of-the-art review of machine learning (ML) methods and applications used in smart grids to predict and optimise energy management. The article discusses the challenges facing smart grids, and how ML can help address them, using a new taxonomy to categorise ML models by method and domain. It describes the different ML techniques used in smart grids as well as examining various smart grid use cases, including demand response, energy forecasting, fault detection, and grid optimisation, and explores how ML can improve these cases. The article proposes a new taxonomy for categorising ML models and evaluates their performance based on accuracy, interpretability, and computational efficiency. Finally, it discusses some of the limitations and challenges of using ML in smart grid applications and attempts to predict future trends. Overall, the article highlights how ML can enable efficient and reliable smart grid systems.

" ["hu_HU"]=> string(891) "

This article presents a state of the art review on machine learning (ML) methods and applications used in smart grids to predict and optimize energy management. The article discusses the challenges faced by smart grids and how ML can help, using a new taxonomy to categorize ML models by method and domain. It explains different ML techniques used in smart grids. It examines various smart grid use cases, including demand response, energy forecasting, fault detection, and grid optimization, and how ML can improve these cases. The article proposes a new taxonomy to categorize ML models and evaluates their performance based on accuracy, interpretability, and computational efficiency. Finally, it discusses limitations, challenges and future trends of using ML in smart grid applications. Overall, the article highlights how ML can enable efficient and reliable smart grid systems.

" } ["copyrightHolder"]=> array(1) { ["en_US"]=> string(108) "Rituraj Rituraj, Várkonyi T. Dániel, Amir Mosavi, Pap József, Várkonyi-Kóczy R. Annamária, Makó Csaba" } ["subtitle"]=> array(2) { ["en_US"]=> string(75) "A Systematic Review, Novel Taxonomy, and Comparative Performance Evaluation" ["hu_HU"]=> string(75) "A Systematic Review, Novel Taxonomy, and Comparative Performance Evaluation" } ["title"]=> array(2) { ["en_US"]=> string(31) "Machine Learning in Smart Grids" ["hu_HU"]=> string(31) "Machine Learning in Smart Grids" } ["locale"]=> string(5) "en_US" ["authors"]=> array(6) { [0]=> object(Author)#855 (6) { ["_data"]=> array(15) { ["id"]=> int(9667) ["email"]=> string(20) "dul.janos@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6999) ["seq"]=> int(2) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(7) "Rituraj" ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(7) "Rituraj" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [1]=> object(Author)#846 (6) { ["_data"]=> array(15) { ["id"]=> int(9668) ["email"]=> string(20) "dul.janos@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6999) ["seq"]=> int(2) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(12) "Várkonyi T." ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(7) "Dániel" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [2]=> object(Author)#852 (6) { ["_data"]=> array(15) { ["id"]=> int(8660) ["email"]=> string(28) "Mosavi.Amirhosein@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6999) ["seq"]=> int(2) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(4) "Amir" ["hu_HU"]=> string(4) "Amir" } ["givenName"]=> array(2) { ["en_US"]=> string(6) "Mosavi" ["hu_HU"]=> string(6) "Mosavi" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [3]=> object(Author)#857 (6) { ["_data"]=> array(15) { ["id"]=> int(9669) ["email"]=> string(20) "dul.janos@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6999) ["seq"]=> int(2) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(3) "Pap" ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(7) "József" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [4]=> object(Author)#861 (6) { ["_data"]=> array(15) { ["id"]=> int(9670) ["email"]=> string(27) "varkonyi-koczy@uni-obuda.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6999) ["seq"]=> int(2) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(19) "Várkonyi-Kóczy R." ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(10) "Annamária" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [5]=> object(Author)#853 (6) { ["_data"]=> array(15) { ["id"]=> int(9671) ["email"]=> string(21) "mako.csaba@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6999) ["seq"]=> int(2) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(5) "Makó" ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(5) "Csaba" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(2) { ["hu_HU"]=> array(2) { [0]=> string(16) "machine learning" [1]=> string(10) "smart grid" } ["en_US"]=> array(6) { [0]=> string(16) "machine learning" [1]=> string(10) "smart grid" [2]=> string(23) "artificial intelligence" [3]=> string(8) "big data" [4]=> string(14) "soft computing" [5]=> string(12) "data science" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#856 (7) { ["_submissionFile"]=> NULL ["_data"]=> array(9) { ["submissionFileId"]=> int(34646) ["id"]=> int(5947) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(6999) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }
PDF
object(Publication)#82 (6) { ["_data"]=> array(28) { ["id"]=> int(6812) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2024-06-28" ["lastModified"]=> string(19) "2024-07-17 10:31:38" ["primaryContactId"]=> int(8416) ["sectionId"]=> int(17) ["seq"]=> int(3) ["submissionId"]=> int(6688) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(3727) "BATHAEE, Yavar (2020): Artificial Intelligence Opinion Liability. Berkeley Technology Law Journal, 35(1), 113–170. Online: https://doi.org/10.15779/Z38P55DH32 BENLI, Erman – ŞENEL, Gayenur (2020): Artificial Intelligence and Tort Law. Ankara Sosyal Bilimler Universitesi Hukuk Fakultesi Dergisi (ASBU Law Journal), 2(2), 296–336. Online: https://doi.org/10.47136/asbuhfd.713190 CYMAN, Damian – GROMOVA, Elizaveta – JUCHNEVICIUS, Edvardas (2021): Regulation of Artificial Intelligence in BRICS and the European Union. BRICS Law Journal, 8(1), 86–115. Online: https://doi.org/10.21684/2412-2343-2021-8-1-86-115 EBERS, Martin (2021): Liability for Artificial Intelligence and EU Consumer Law. Journal of Intellectual Property, Information Technology and Electronic Commerce Law, 12(2), 204–220. European Commission’s High-Level Expert Group on Artificial Intelligence (2019): Ethics Guidelines for Trustworthy AI. Brussels: European Commission. HEISS, Stefan (2021): Towards Optimal Liability for Artificial Intelligence: Lessons from the European Union’s Proposal of 2020. Hastings Science and Technology Law Journal, 12(2), 186–224. LAI, Alicia (2021): Artificial Intelligence, LLC: Corporate Personhood as Tort Reform, Michigan State Law Review, 2021(2), 597-654. MARTIN-CASALS, Miquel (2010): Technological Change and the Development of Liability for Fault: A General Introduction. In MARTIN-CASALS, Miquel (ed.): The Development of Liability In Relation to Technological Change. New York: Cambridge University Press, 1–39. MIKLIČ, Samo (2021): Non-Contractual Liability and Artificial Intelligence, Pravni Letopis, 2021(1), 67–82. MIREILLE, Hildebrandt (2020): The Artificial Intelligence of European Union Law. German Law Journal, 21(1), 74–79. Online: https://doi.org/10.1017/glj.2019.99 ROBINSON, Keith W. (2022): Enabling Artificial Intelligence. Houston Law Review, 60(2), 331–362. SEE, Benedict (2021): Paging Doctor Robot: Medical Artificial Intelligence, Tort Liability, and Why Personhood May Be the Answer. Brooklyn Law Review, 87(1), 417–443. European Commission (2020a): Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: A European Strategy for Data. COM(2020) 66 final. Online: European Commission (2020b): Proposal for a Regulation of the European Parliament and of the Council on European Data Governance (Data Governance Act). COM(2020) 767 final. Online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52020PC0767 European Commission (2021): Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts. COM(2021) 206 final. Online: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52021PC0206 European Commission (2022): Proposal for a Regulation of the European Parliament and of the Council on Harmonised Rules on Fair Access to and Use of Data (Data Act). COM(2022) 68 final. Online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2022%3A68%3AFIN Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Council Directive 85/374/EEC of 25 July 1985 on the approximation of the laws, regulations and administrative provisions of the Member States concerning liability for defective products California Consumer Privacy Act of 2018 (1798.100 – 1798.199.100). " ["copyrightYear"]=> int(2024) ["issueId"]=> int(594) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(5) "85-99" ["pub-id::doi"]=> string(21) "10.32575/ppb.2024.1.4" ["abstract"]=> array(1) { ["en_US"]=> string(1007) "

The dynamic evolution of artificial intelligence (AI) and machine learning (ML) tools poses challenges to the existing liability concepts. This paper aims to examine some of the fields of tortious liability that are most affected by these developments to analyse whether the existing legal standards in civil liability can still be used, with slight reinterpretation, when approaching liability scenarios related to AI and ML, and whether fine tuning of the existing liability regimes is needed, or novel liability scenarios should be established. To answer this question, the paper begins by examining the nature of the regulation of AI and ML: whether it should be a regulatory regime neutral to technology or whether, instead, a sector specific approach is essential. The study considers the already existing legal authorities of the EU and the U.S. as starting points for the analysis, and briefly examines the interpretations municipal courts apply when deciding in AI and ML related tort cases.

" } ["copyrightHolder"]=> array(1) { ["en_US"]=> string(12) "Fézer Tamas" } ["title"]=> array(1) { ["en_US"]=> string(67) "Upside Down: Liability, Risk Allocation and Artificial Intelligence" } ["locale"]=> string(5) "en_US" ["authors"]=> array(1) { [0]=> object(Author)#854 (6) { ["_data"]=> array(15) { ["id"]=> int(8416) ["email"]=> string(25) "fezer.tamas@law.unideb.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6812) ["seq"]=> int(3) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(48) "a:1:{s:5:"en_US";s:22:"University of Debrecen";}" ["hu_HU"]=> string(48) "a:1:{s:5:"en_US";s:22:"University of Debrecen";}" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(6) "Fézer" ["hu_HU"]=> string(6) "Fézer" } ["givenName"]=> array(2) { ["en_US"]=> string(6) "Tamás" ["hu_HU"]=> string(6) "Tamás" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(1) { ["en_US"]=> array(5) { [0]=> string(23) "artificial intelligence" [1]=> string(14) "tort liability" [2]=> string(17) "product liability" [3]=> string(18) "European Union law" [4]=> string(16) "strict liability" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#865 (7) { ["_submissionFile"]=> NULL ["_data"]=> array(9) { ["submissionFileId"]=> int(34647) ["id"]=> int(5948) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(6812) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }
PDF
object(Publication)#86 (6) { ["_data"]=> array(28) { ["id"]=> int(6829) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2024-06-28" ["lastModified"]=> string(19) "2024-07-17 10:32:50" ["primaryContactId"]=> int(8436) ["sectionId"]=> int(17) ["seq"]=> int(4) ["submissionId"]=> int(6705) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(10698) "BIBÓ, István (1986): Az európai társadalomfejlődés értelme [The Meaning of European Social Development]. In Válogatott tanulmányok 3. kötet [Selected Studies, Volume 3]. Budapest: Magvető, 7–122. BOEHMER, Christiansen (2002): The Precautionary Principle in Germany – Enabling Government. In O’RIORDAN, Timothy – CAMERON, James (eds.): Interpreting the Precautionary Principle. London: Routledge, 31–61. Online: https://doi.org/10.4324/9781315070490 BROWNSWORD, Roger – SCOTFORD, Eloise – YEUNG, Karen (2016): Law, Regulation, and Technology: The Field, Frame, and Focal Questions. In BROWNSWORD, Roger – SCOTFORD, Eloise – YEUNG, Karen (eds.): The Oxford Handbook of Law, Regulation, and Technology. Oxford: Oxford University Press, 3–38. Brownsword, Roger: Law 3.0 Routledge, London, 2021 BROWNSWORD, Roger (2011): Lost in Translation: Legality, Regulatory Margins, and Technological Management. Berkeley Technology Law Journal, 26(3), 1321–1366. BROWNSWORD, Roger (2005): Code, Control, and Choice: Why East is East and West is West. Legal Studies, 25(1), 1–21. Online: https://doi.org/10.1111/j.1748-121X.2005.tb00268.x CAUFFMAN, Caroline – GOANTA Catalina (2021): A New Order: The Digital Services Act and Consumer Protection. European Journal of Risk Regulation, 12(4), 758–774. Online: https://doi.org/10.1017/err.2021.8 COHEN, Julie (2017): Between Truth and Power. The Legal Constructions of Informational Capitalism. New York: Oxford University Press. DARLEY, John M. – ROBINSON, Paul H. – CARLSMITH, Kevin M. (2001): The Ex Ante Function of the Criminal Law. Law & Society Review, 35(1), 165–190. Online: https://doi.org/10.2307/3185389 EHRLICH, Eugen (2022): Grundlegung des Soziologie des Rechts (5th edition). Berlin: Duncker & Humblot. European Commission (2001): European Governance: A White Paper. COM (2001) 428. Online: https://ec.europa.eu/commission/presscorner/detail/en/DOC_01_10 European Commission (2002): Communication from the Commission of 5 June 2002, Action Plan “Simplifying and improving the regulatory environment”. COM (2002) 278 final. Online: https://eur-lex.europa.eu/EN/legal-content/summary/action-plan-for-better-regulation.html European Commission (2020): Proposal Regulation of the European Parliament and of the Council on European Data Governance (Data Governance Regulation). COM (2020) 767 final. Online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52020PC0767 European Commission (2022): Proposal for a Regulation of the European Parliament and of the Council on Harmonised Rules on Fair Access to and Use of Data (Data Sharing Legislation). COM (2022) 68 final. Online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2022%3A68%3AFIN DUMBRAVA, Costica (2021): Key Social Media Risks to Democracy: Risks from Surveillance, Personalisation, Disinformation, Moderation and Microtargeting. European Parliamentary Research Service. Online: https://doi.org/10.2861/135170 Facebook’s policy on dangerous individuals and organisations https://transparency.fb.com/hu-hu/policies/community-standards/dangerous-individuals-organizations/ FRIED, Barbara H. (2003): Ex Ante/Ex Post. Journal of Contemporary Legal Issues, 13(1), 123–160. Online: https://doi.org/10.2139/ssrn.390462 GALLE, Brian (2015): In Praise of Ex Ante Regulation. Vanderbilt Law Review, 68(6), 1715– 1759. GARDBAUM, Stephen (2003): The Horizontal Effect of Constitutional Rights. Michigan Law Review, 102(3), 387–459. Online: https://doi.org/10.2307/3595366 Habermas, Jürgen (1987) [1981]. Theory of Communicative Action, Volume Two: Lifeworld and System: A Critique of Functionalist Reason (Book). Translated by Thomas A. McCarthy. Boston, Mass.: Beacon Press HARREMOËS, Poul – GEE, David – MACGARVIN, Malcolm – STIRLING, Andy – KEYS, Jane – WYNNE, Brian – VAZ, Sofia Guedes eds. (2001): Late Lessons from Early Warnings. The Precautionary Principle 1896–2000. Luxembourg: Office for Official Publications of the European Communities. HILDEBRANDT, Mireille (2018): Primitives of Legal Protection in the Era of Data-Driven Platforms. Online: https://doi.org/10.2139/ssrn.3140594 JOH, Elizabeth E. (2016–2017): Policing Police Robots. UCLA Law Review Discourse, 64, 516–543. KEYNES, John M. (1936): The general theory of employment, interest and money. First Harvest/Harcourt, San Diego – London – New York KOLSTADT, Charles D. – ULEN, Thomas S. – JOHNSON, Gary V. (1990): Ex Post Liability for Harm vs Ex Ante Safety Regulation: Substitutes guard Complements? The American Economic Review, 80(1), 888–901. KORNAI, János (1992): The Socialist System: The Political Economy of Communism. Princeton, NJ: Princeton University Press. MEFFORD, Aron (1997): Lex Informatica: Foundations of Law on the Internet. Indiana Journal of Global Legal Studies, 5(1), 211–238. MISES, Ludwig von (1944): Bureaucracy. New Haven: Yale University Press. O’RIORDAN, Tim – CAMERON, James eds. (2002): Interpreting the Precautionary Principle. London: Routledge. PHILLIPSON, Gavin (1999): The Human Rights Act, Horizontal Effect and the Common Law: a Bang or a Whimper. Modern Law Review, 62(6), 824–849. Online: https://doi.org/10.1111/1468-2230.00240 POLANYI, Karl (2001): The Great Transformation. The Political and Economic Origins of Our Times. Boston, MA: Beacon Press. RAZ, Joseph (1990): Practical Reason and Norms. Princeton, NJ: Princeton University Press. REHBINDER, Manfred (1986): Die Begründung der Rechtssoziologie durch Eugen Ehrlich. Berlin: Dunckert & Humblot. REIDENBERG, Joel R. (1997): Lex Informatica: The Formulation of Information Policy Rules through Technology. Texas Law Review, 76(3), 553–594. SCHLESINGER, Philip (2022): The Neo-Regulation of Internet Platforms in the United Kingdom. Policy & Internet, 14 (1), 47–62. Online: https://doi.org/10.1002/poi3.288 SENDEN, Linda (2005): Soft Law, Self- regulation and Co-regulation in European Law: Where do they Meet? Electronic Journal of Comparative Law, 9(1). Online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=943063 STARK, David – PAIS, Ivana (2020): Algorithmic Management in the Platform Economy. Sociologica, 14(3), 47–72. Online: https://doi.org/10.6092/issn.1971-8853/12221 SUNSTEIN, Cass (2005): Laws of Fear. Beyond the Precautionary Principle. Cambridge: Cambridge University Press. Online: https://doi.org/10.1017/CBO9780511790850 SUSSKIND, Richard (2009): The End of Lawyers? Rethinking the Nature of Legal Services. Oxford: Oxford University Press. TUSHNET, Mark (2003): The Issue of State Action/Horizontal Effect in Comparative Constitutional Law. International Journal of Constitutional Law, 1(1), 79–98. Online: https://doi.org/10.1093/icon/1.1.79 YEUNG, Karen – LODGE, Martin (2019): Introduction. In YEONG, Karen – LODGE, Martin (eds): Algorithmic Regulation. Oxford: Oxford University Press. Online: https://doi.org/10.1093/oso/9780198838494.003.0001 VAN DIJCK, José – NIEBORG, David – POELL, Thomas (2019): Reframing Platform Power. Internet Policy Review, 8(2). Online: https://doi.org/10.14763/2019.2.1414 VICARELLI, Fausto (1984): Keynes: The Instability of Capitalism. [s. l.]: University of Pennsylvania Press. Online: https://doi.org/10.9783/9781512819892 WALKER, Neil (2005): Legal Theory and the European Union European University Institute. EUI Working Papers, Law 2005/16. Online: https://doi.org/10.2139/ssrn.891032 ZŐDI, Zsolt (2022): Algorithmic Explainability and Legal Reasoning. The Theory and Practice of Legislation, 10(1), 67–92. Online: https://doi.org/10.1080/20508840.2022.2033945 Legal sources Commission Delegated Regulation (EU) 2019/945 of 12 March 2019 on unmanned aircraft systems and third country operators of unmanned aircraft systems Communication Decency Act of the USA Council Directive 93/13/EEC of 5 April 1993 on unfair terms in consumer contracts Directive – Consumer Protection Directive Council Directive 93/13/EEC (April 5, 1993) on unfair terms in consumer contracts Directive (EU) 2018/1808 of the European Parliament and of the Council of 14 November 2018 amending Directive 2010/13/EU on the coordination of certain provisions laid down by law, regulation or administrative action in Member States concerning the provision of audiovisual media services (Audiovisual Media Services Directive) in view of changing market realities Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC (Copyright Directive) Directive 2000/31/EC of the European Parliament and of the Council of 8 June 2000 on certain legal aspects of information society services, in particular electronic commerce, in the Internal Market (‘Directive on electronic commerce’) Directive 2010/13/EU of the European Parliament and of the Council of 10 March 2010 on the coordination of certain provisions laid down by law, regulation or administrative action in Member States concerning the provision of audiovisual media services (Audiovisual Media Services Directive – old AVMSD) Network-enforcement law (Netzwerkdurchsetzungsgezetz – NetzDG) of Germany Proposal for a Directive of the European Parliament and of the Council on improving working conditions in platform work COM(2021) 762 final (Platform work proposal). Online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2021%3A762%3AFIN Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain union legislative acts Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation – GDPR) Regulation (EU) 2019/1150 of the European Parliament and of the Council of 20 June 2019 on promoting fairness and transparency for business users of online intermediation services (Platform-to-Business Regulation – P2B) Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on contestable and fair markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 (Digital Markets Act – DMA) Regulation (EU) 2022/2065 of the European Parliament and of the Council of 19 October 2022 on a Single Market For Digital Services and amending Directive 2000/31/EC (Digital Services Act – DSA) Shelley v. Kraemer, 334 U.S. 1 (1948)" ["copyrightYear"]=> int(2024) ["issueId"]=> int(594) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(7) "101-125" ["pub-id::doi"]=> string(21) "10.32575/ppb.2024.1.5" ["abstract"]=> array(2) { ["en_US"]=> string(1126) "

This paper discusses the recently emerging platform law from a jurisprudential point of view. After defining the platform as a general coordination mechanism, it deals with topics such as the rationale for regulation, its main goals, and its general characteristics. According to the study, the main argument for regulation is that the platform, as a coordination mechanism, tends to become unstable without intervention, or to become harmful from the point of view of society. Above all, it tends to abuse the asymmetric power situation that exists between the platform and its users. These conditions must be prevented from occurring, and platform users must be protected in certain situations. The study lists four features that characterise platform law: its ex ante regulatory nature, the predominance of technology regulation and self-regulation, and the extensive use of user protection tools, such as complaint mechanisms, protection of user accounts, and explainability obligations. This toolbox partly resembles the long-established methods of consumer protection, but it also differs from it in certain ways.

" ["hu_HU"]=> string(1224) "

A cikk a közelmúltban kialakuló platformjogot jogelméleti szempontból tárgyalja. A platform általános koordinációs mechanizmusként való meghatározása után olyan témákkal foglalkozik, mint a szabályozás logikája,  fő céljai, általános jellemzői. A szabályozás fő indokaként a tanulmány azt állítja, hogy a platform, mint koordinációs mechanizmus beavatkozás nélkül hajlamos instabillá válni, illetve a társadalom szempontjából káros állapotba kerülni. Mindenekelőtt hajlamos visszaélni a platform és felhasználói között fennálló aszimmetrikus hatalmi helyzettel. Ezeket az állapotokat meg kell akadályozni, és bizonyos helyzetekben meg kell védeni a felhasználókat. A tanulmány négy olyan jellemzőt sorol fel, amelyek a platformjogot jellemzik: az ex ante szabályozási jelleg, a technológiai szabályozás és önszabályozás túlsúlya, valamint a felhasználóvédelmi eszközök, így a panaszmechanizmusok, a felhasználói fiókok védelme és a megmagyarázhatósági kötelezettségek kiterjedt alkalmazása. Ez utóbbi eszköztár részben hasonlít a fogyasztóvédelem régóta ismert módszereire, de bizonyos ponton el is tér attól.

" } ["copyrightHolder"]=> array(1) { ["en_US"]=> string(11) "Ződi Zsolt" } ["title"]=> array(2) { ["en_US"]=> string(30) "A Legal Theory of Platform Law" ["hu_HU"]=> string(5) "angol" } ["locale"]=> string(5) "en_US" ["authors"]=> array(1) { [0]=> object(Author)#859 (6) { ["_data"]=> array(10) { ["id"]=> int(8436) ["email"]=> string(21) "Zodi.Zsolt@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6829) ["seq"]=> int(4) ["userGroupId"]=> int(116) ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(5) "Ződi" ["hu_HU"]=> string(5) "Ződi" } ["givenName"]=> array(2) { ["en_US"]=> string(5) "Zsolt" ["hu_HU"]=> string(5) "Zsolt" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(2) { ["hu_HU"]=> array(1) { [0]=> string(5) "Angol" } ["en_US"]=> array(4) { [0]=> string(19) "platform regulation" [1]=> string(20) "Digital Services Act" [2]=> string(19) "theory of platforms" [3]=> string(23) "coordination mechanisms" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#848 (7) { ["_submissionFile"]=> NULL ["_data"]=> array(9) { ["submissionFileId"]=> int(34648) ["id"]=> int(5949) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(6829) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }
PDF
object(Publication)#847 (6) { ["_data"]=> array(28) { ["id"]=> int(6793) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2024-06-28" ["lastModified"]=> string(19) "2024-07-17 10:00:40" ["primaryContactId"]=> int(8396) ["sectionId"]=> int(17) ["seq"]=> int(5) ["submissionId"]=> int(6669) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(5431) "BALDWIN, Richard (2019): The Globotics Upheaval. Globalization, Robotics, and the Future of Work. New York: Oxford University Press. BOES, Andreas – KÄMPF, Tobias – LANGES, Barbara – LŰHR, Thomas (2017): The Disruptive Power of Digital Transformation. In BRIKEN, Kendra – CHILLAS, Shiona – KRZYWDZINSKI, Martin – MARKS, Abigail (eds.): The New Digital Workplace. How New Technologies Revolutionise Work. London: Red Globe Press, 153–173. BRYNJOLFSSON, Erik – MCAFEE, Andrew (2014): The Second Machine Age. Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W.W. Norton. FIELD, Justin Craig – CHAN, Xi (2018): Contemporary Knowledge Workers and the Boundaryless Work–Life Interface. Implications for the Human Resource Management of the Knowledge Workforce. Frontiers in Psychology, 9(2414), 1–10. Online: https://doi.org/10.3389/fpsyg.2018.02414 FREY, Carl (2019): The Technology Trap. Capital, Labor, and Power in the Age of Automation. Princeton: Princeton University Press. Online: https://doi.org/10.1515/9780691191959 FREY, Carl – OSBORNE, Michael (2017): The Future of Employment. How Susceptible Are Jobs to Computerisation? Technological Forecasting and Social Change, 114, 254–280. Online: https://doi.org/10.1016/j.techfore.2016.08.019 HELSPER, Ellen Johanna (2012): A Corresponding Fields Model for the Links between Social and Digital Exclusion. Communication Theory, 22(4), 403–426. Online: https://doi.org/10.1111/j.1468-2885.2012.01416.x HEPONIEMI, Tarja – GLUSCHKOFF, Kia – LEEMANN, Lars – MANDERBACKA, Kristiina – AALTO, Anna-Mari – HYPPÖNEN, Hannele (2023): Digital Inequality in Finland. Access, Skills and Attitudes as Social Impact Mediators. New Media & Society, 25(9), 2475–2491. Online: https://doi.org/10.1177/14614448211023007 HYPPÖNEN, Hannele – ILMARINEN, Katja (2016): Sosiaali- ja terveydenhuollon digitalisaatio. Tutkimuksesta tiiviisti 22/2016. Helsinki: Terveyden ja hyvinvoinnin laitos. KORJONEN-KUUSIPURO, Kristiina – RASI-HEIKKINEN, Päivi – VUOJÄRVI, Hanna – PIHLAINEN, Kaisa – KÄRNÄ, Eija eds. (2022): Ikääntyvät digiyhteiskunnassa. Elinikäisen oppimisen mahdollisuudet. Helsinki: Gaudeamus. MAZZUCATO, Mariana (2021): Mission Economy. A Moonshot Guide to Changing Capitalism. Milton Keynes: Allen Lane. NEFF, Gina – NAGY, Peter (2019): Agency in the Digital Age. Using Symbiotic Agency to Explain Human–Technology Interaction. In PAPACHARISSI, Zizi (ed.): A Networked Self and Human Augmentics, Artificial Intelligence, Sentience. New York: Routledge, 97–107. Online: https://doi.org/10.4324/9781315202082-8 PwC (2018): Will Robots Really Steal Our Jobs? An International Analysis of the Potential Long Term Impact of Automation. PricewaterhouseCoopers. RAGNEDDA, Massimo – MUSCHERT, Glenn eds. (2018): Theorizing Digital Divides. London: Routledge. Online: https://doi.org/10.4324/9781315455334 SAIKKONEN, Loretta (2022): Metallialan työntekijöiden digitaaliset informaatiotaidot – ketkä ovat vaarassa digisyrjäytyä? Työelämän Tutkimus, 20(3), 385–410. Online: https://doi.org/10.37455/tt.110005 SAIKKONEN, Loretta – KAARAKAINEN, Meri-Tuulia (2021): Multivariate Analysis of Teachers’ Digital Information Skills. The Importance of Available Resources. Computers & Education, 168(104206), 1–13. Online: https://doi.org/10.1016/j.compedu.2021.104206 SAK (2020): SAK:n työolobarometri. Helsinki: SAK. SCHEERDER, Anique – VAN DEURSEN, Alexander– VAN DIJK, Jan (2017): Determinants of Internet Skills Use and Outcomes. A Systematic Review of the Second- and Third-Level Digital Divide. Telematics and Informatics, 34(8), 1607–1624. Online: https://doi.org/10.1016/j.tele.2017.07.007 SUTELA, Hanna – PÄRNÄNEN, Anna – KEYRILÄINEN, Marianne (2019): Digiajan työelämä. Työolotutkimuksen tuloksia 1977–2018. Helsinki: Statistics Finland. VAN DEURSEN, Alexander – HELSPER, Ellen (2018): Collateral Benefits of Internet Use. Explaining the Diverse Outcomes of Engaging with the Internet. New Media & Society, 20(7), 2333–2351. Online: https://doi.org/10.1177/1461444817715282 VAN DEURSEN, Alexander – HELSPER, Ellen – EYNON, Rebecca (2016): Development and Validation of the Internet Skills Scale (ISS). Information, Communication & Society, 19(6), 804–823. Online: https://doi.org/10.1080/1369118X.2015.1078834 VAN DEURSEN, Alexander – HELSPER, Ellen – EYNON, Rebecca – VAN DIJK, Jan (2017): The Compoundness and Sequentiality of Digital Inequality. International Journal of Communication, 11. 452–473. VAN DIJK, Jan A.G.M. (2005): The Deepening Divide, Inequality in the Information Society. London: Sage. Online: https://doi.org/10.4135/9781452229812 VAN LAAR, Ester – VAN DEURSEN, Alexander – VAN DIJK, Jan – DE HAAN, Jos (2017): The Relation between 21st-Century Skills and Digital Skills. A Systematic Literature Review. Computers in Human Behavior, 72, 577–588. Online: https://doi.org/10.1177/2158244019900176 VUORIKARI, Riina – KLUZER, Stefano – PUNIE, Yves (2022): DigComp 2.2. The Digital Competence Framework for Citizens – With New Examples of Knowledge, Skills and Attitudes. JRC128415. Luxembourg: Publications Office of the European Union. Online: https://doi.org/10.2760/115376 WESTLUND, Oscar – BJUR, Jakob (2014): Media Life of the Young. Young, 22(1), 21–41. Online: https://doi.org/10.1177/1103308813512934" ["copyrightYear"]=> int(2024) ["issueId"]=> int(594) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(7) "127-144" ["pub-id::doi"]=> string(21) "10.32575/ppb.2024.1.6" ["abstract"]=> array(1) { ["en_US"]=> string(1056) "

Based on Statistics Finland’s Quality of Work Life Survey 2018, this paper seeks how Finnish employees’ use of digital tools differs from each other, what sociodemographic and work contextrelated factors these differences are connected to, and how differences in usage are reflected in the effects of digitalisation on employees’ work. The research identified five user groups. Nearly half of the employees are classified as Skilled Users, which are typically of a young age. Challenges for other groups include deficiencies in digital skills, problems in learning to use digital tools, routine-like usage, low learning demands at work, and a high workload and learning pressure arising from intensive use of digital tools. The results support the sequential and compound digital exclusion arguments derived from previous literature, but do not fully support the stratification argument. The paper shows that among employees there are digital divides of various types. Narrowing these gaps requires different policies and customised solutions.

" } ["copyrightHolder"]=> array(1) { ["en_US"]=> string(14) "Tuomo Alasoini" } ["title"]=> array(1) { ["en_US"]=> string(69) "Digital Tools User Groups as a Digital Divide Among Finnish Employees" } ["locale"]=> string(5) "en_US" ["authors"]=> array(1) { [0]=> object(Author)#851 (6) { ["_data"]=> array(15) { ["id"]=> int(8396) ["email"]=> string(21) "tuomo.alasoini@ttl.fi" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6793) ["seq"]=> int(5) ["userGroupId"]=> int(116) ["country"]=> string(2) "FI" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(66) "a:1:{s:5:"en_US";s:40:"Finnish Institute of Occupational Health";}" ["hu_HU"]=> string(66) "a:1:{s:5:"en_US";s:40:"Finnish Institute of Occupational Health";}" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(5) "Tuomo" ["hu_HU"]=> string(5) "Tuomo" } ["givenName"]=> array(2) { ["en_US"]=> string(8) "Alasoini" ["hu_HU"]=> string(8) "Alasoini" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(1) { ["en_US"]=> array(7) { [0]=> string(14) "Digital divide" [1]=> string(17) "Digital exclusion" [2]=> string(13) "Digital skill" [3]=> string(12) "Digital tool" [4]=> string(14) "Digitalization" [5]=> string(10) "Inequality" [6]=> string(11) "Usage group" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#868 (7) { ["_submissionFile"]=> NULL ["_data"]=> array(9) { ["submissionFileId"]=> int(34649) ["id"]=> int(5950) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(6793) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }
PDF
object(Publication)#863 (6) { ["_data"]=> array(29) { ["id"]=> int(6854) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2024-06-28" ["lastModified"]=> string(19) "2024-07-18 11:11:26" ["primaryContactId"]=> int(8461) ["sectionId"]=> int(17) ["seq"]=> int(6) ["submissionId"]=> int(6730) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(6029) "Aleksynska, Mariya (2021): Digital Work in Eastern Europe: Overview of Trends, Outcomes and Policy Responses. ILO Working Paper 32. Geneva: ILO. Anđelković, Branka – Jakobi, Tanja (2022): Embracing the Digital Age: The Future of Work in the Western Balkans – Serbia. European Training Foundation. Online: https://www.etf.europa.eu/en/document-attachments/embracing-digital-age-future-workwestern-balkans-serbia Anđelković, Branka – Jakobi, Tanja – Ivanović, Vladan – Kalinić, Zoran – Radonjić, Ljubivoje (2021a): Gigmetar Region. Public Policy Research Center. Online: http://gigmetar.publicpolicy.rs/en/region4-2/ Anđelković, Branka – Jakobi, Tanja – Ivanović, Vladan – Kalinić, Zoran – Radonjić, Ljubivoje (2021b): Gigmetar Region. Public Policy Research Center. Online: http://gigmetar.publicpolicy.rs/en/region5-2/ Anđelković, Branka – Jakobi, Tanja – Ivanović, Vladan – Kalinić, Zoran – Radonjić, Ljubivoje (2022a): Gigmetar Region. Public Policy Research Center. Online: http://gigmetar.publicpolicy.rs/en/region-en-2022-1/. Anđelković, Branka – Jakobi, Tanja – Ivanović, Vladan – Kalinić, Zoran – Radonjić, Ljubivoje (2022b): Gigmetar Region. Public Policy Research Center. Online: http://gigmetar.publicpolicy.rs/en/region-en-2022-1/. Cedefop (2020): Developing and Matching Skills in the Online Platform Economy: Findings on New Forms of Digital Work and Learning from Cedefop’s CrowdLearn Study. Luxembourg: Publications Office of the European Union. Online: https://doi.org/10.2801/588297 ČOLOVIĆ, Petar – ANĐELKOVIĆ, B. – JAKOBI, Tanja (2021): How Many Online Workers Are There in Serbia? First National Assessment of the Number of the Online Workers on Global Digital Platforms. Belgrade: Public Policy Research Center. Online: https://publicpolicy.rs/publikacije/bac4e207bba2e8a9fa84e063c954710e02b375ce.pdf FORRIER, Anneleen – SELS, Luc – STYNEN, Dave (2010): Career Mobility at the Intersection between Agent and Structure: A Conceptual Model. Journal of Occupational and Organizational Psychology, 82, 739–759. Online: https://doi.org/10.1348/096317909X470933 GRAHAM, Mark – ANWAR, Mohammad (2019): The Global Gig Economy: Towards a Planetary Labour Market? First Monday, 24(4). Online: https://doi.org/10.5210/fm.v24i4.9913 ILO (2021): World Employment and Social Outlook 2021: The Role of Digital Labour Platforms in Transforming the World of Work. Geneva: International Labour Office. Online: https://www.ilo.org/global/research/global-reports/weso/2021/WCMS_771749/lang--en/index.htm KENNEY, Martin – ZYSMAN, John (2019): The Platform Economy and Geography: Restructuring the Space of Capitalist Accumulation. Online: https://doi.org/10.2139/ssrn.3497978 KENNEY, Martin – ZYSMAN, John – BEARSON, Dafna – CARLTON, Camille (2023): 13. Spatial Implications of the Platform Economy: Cases and Questions. In BIANCHI, Patrizio – LABORY, Sandrine – TOMLISON, Philip R. (eds.): Handbook of Industrial Development. [s. l.]: Edward Elgar, 215–231. Online: https://doi.org/10.4337/9781800379091.00023 KUEK, Siou C. – PARADI-GUILFORD, Cecilia – FAYOMI, Toks – IMAIZUMI, Saori – IPEIROTIS, Panos – PINA, Patricia – SINGH, Manpreet (2015): The Global Opportunity in Online Outsourcing. The World Bank Group. Online: https://econpapers.repec.org/paper/wbkwboper/22284.htm LEHDONVIRTA, Vili – KÄSSI, Otto – HJORTH, Isis – BARNARD, Helena – GRAHAM, Mark (2019): The Global Platform Economy: A New Offshoring Institution Enabling Emerging-Economy Microproviders. Journal of Management, 45(2), 567–599. Online: https://doi.org/10.1177/0149206318786781 MANDL, Irene – KILHOFFER, Zachary – LENAERTS, Karolien – DE GROEN, Willem P. (2018): Employment and Working Conditions of Selected Types of Platform Work. Eurofound Research Report. Luxembourg: Publications Office of the European Union. Online: https://doi.org/10.2806/42948 MCDONNELL, A. – CARBERY, R. – BURGESS, J. – SHERMAN, U. (2021): Technologically Mediated Human Resource Management in the Gig Economy. The International Journal of Human Resource Management, 32(19), 3995–4015. Online: https://doi.org/10.1080/09585192.2021.1986109 MILES, Raymond E. – SNOW, Charles C. (1996): Twenty-First-Century Careers. In ARTHUR, Michael B. – ROUSSEAU, Dennis M. (eds.): The Boundaryless Career: A New Employment Principle for a New Organizational Era. New York: Oxford University Press, 97–115. Online: https://doi.org/10.1093/oso/9780195100143.003.0006 MONIZ, António B. – BOAVIDA, Nuno – MAKÓ, Csaba – KRINGS, Bettina J. – MIGUEL, Pablo S. D. (2021): Digital Labour Platforms: Representing Workers in Europe. Vila Nova de Famalicão: Húmus–CICS.NOVA. Online: https://doi.org/10.34619/rwrm-3uun OLI (2018): Online Labour Index, Online: http://onlinelabourobservatory.org/oli-supply/ OLI (2022): Online Labour Index, Online: http://onlinelabourobservatory.org/oli-supply/ PAJARINEN, Mika – ROUVINEN, Petri – CLAUSSEN, Jörg – HAKANEN, Jari – KOVALAINEN, Anne – KRETSCHMER, Tobias – POUTANEN, Seppo – SEIFRIED, Mareike – SEPPÄNEN, Laura (2018): Upworkers in Finland: Survey Results (ETLA Reports No. 85). The Research Institute of the Finnish Economy. Online: https://econpapers.repec.org/paper/rifreport/85.htm PIASNA, Agnieszka (2020): Counting Gigs: How Can We Measure the Scale of Online Platform Work? ETUI Working Paper. Brussels: ETUI. Online: https://doi.org/10.2139/ssrn.3699350 PIASNA, Agnieszka – ZWYSEN, Wouter – DRAHOKOUPIL, Jan (2022): The Platform Economy in Europe: Results from the Second ETUI Internet and Platform Work Survey (IPWS). (SSRN Scholarly Paper No. 4042629). Online: https://doi.org/10.2139/ssrn.4042629 SCHOR, Juliet B. – VALLAS, Steven P. (2023): Labour and the Platform Economy. In HEYDARI, Babak – ERGUN, Ozlem – DYAL-CHAND, Rashmi – BART, Jakov (eds.): Reengineering the Sharing Economy: Design, Policy, and Regulation. Cambridge: Cambridge University Press, 83." ["copyrightYear"]=> int(2024) ["issueId"]=> int(594) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(7) "145-172" ["pub-id::doi"]=> string(21) "10.32575/ppb.2024.1.7" ["abstract"]=> array(2) { ["en_US"]=> string(1978) "

This paper focuses on the increasing prominence of digital labour platforms in the labour markets of Southeast Europe, and compares the supply of online labour from nine selected countries: Serbia, Romania, Hungary, Croatia, Bosnia and Herzegovina, Montenegro, Albania, North Macedonia, and Bulgaria. Digital labour platforms, as an innovative business model, play an important role in today’s labour markets by linking the demand and supply of digital work. Southeast Europe is no exception to this trend, and has become an important supplier of online labour. With the impact of the Covid–19 pandemic, this and other new forms of employment further increased both globally and in Southeast Europe. Despite this trend, online labour often remains invisible and under the radar of national policymakers and regulators, as well as national statistical agencies, due to the globalised nature of online platforms. This paper aims to shed light on the development of online labour in the countries studied, based on publicly available data collected through Gigmetar, a web scraping tool designed to monitor trends on the number, gender, incomes, and occupations of online workers. The paper compares online labour from nine countries active on the most significant general digital labour platforms (Upwork, Freelancer, and Guru) from February 2022 to October 2022. The criteria for the comparison include occupations, gender and income. The analysis is based on the data of approximately 80% of the total number of active digital workers on the platforms under investigation.
The paper points out the similarities and differences in online labour between the countries of Southeast Europe. For example, the number of online workers increased in all the countries, with creative services and multimedia and software development comprising the most dominant occupations in each country. Moreover, men are more commonly represented in these digital markets than women.

" ["hu_HU"]=> string(899) "

This paper focuses on the increasing prominence of digital labour platforms in the labour markets of Southeast Europe and compares the supply of online labour from 9 selected countries: Serbia, Romania, Hungary, Croatia, Bosnia and Herzegovina, Montenegro, Albania, North Macedonia, and Bulgaria. Digital labour platforms, as an innovative business model, play an important role in today’s labor markets by linking the demand and supply of digital work. This is no exception in Southeast Europe which has become an important supplier of the online labour. With the COVID-19 pandemic, this and other new forms of employment further increased both globally and in Southeast Europe. Despite this trend, online labour often remains invisible and under the radar of national policy makers and regulators, as well as national statistical agencies due to the global nature of the online platforms.

" } ["copyrightHolder"]=> array(1) { ["en_US"]=> string(52) "Branka Andjelkovic, Tanja Jakobi, Ljubivoje Radonjic" } ["subtitle"]=> array(2) { ["en_US"]=> string(84) "A Comparison of the Growth of Online Labour in Various Countries in Southeast Europe" ["hu_HU"]=> string(71) "The Growth of Online Labour and Country Differences in Southeast Europe" } ["title"]=> array(2) { ["en_US"]=> string(37) "Right Before Your Eyes, Yet Unnoticed" ["hu_HU"]=> string(36) "Right Before Your Eyes Yet Unnoticed" } ["locale"]=> string(5) "en_US" ["authors"]=> array(3) { [0]=> object(Author)#871 (6) { ["_data"]=> array(15) { ["id"]=> int(8461) ["email"]=> string(28) "branka.andjelkovic@gmail.com" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6854) ["seq"]=> int(6) ["userGroupId"]=> int(116) ["country"]=> string(2) "RS" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(6) "Branka" ["hu_HU"]=> string(6) "Branka" } ["givenName"]=> array(2) { ["en_US"]=> string(11) "Andjelkovic" ["hu_HU"]=> string(11) "Andjelkovic" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [1]=> object(Author)#864 (6) { ["_data"]=> array(15) { ["id"]=> int(9672) ["email"]=> string(24) "t.jakobi@publicpolicy.rs" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6854) ["seq"]=> int(6) ["userGroupId"]=> int(116) ["country"]=> string(2) "RS" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(5) "Tanja" ["hu_HU"]=> string(0) "" } ["givenName"]=> array(2) { ["en_US"]=> string(6) "Jakobi" ["hu_HU"]=> string(0) "" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [2]=> object(Author)#877 (6) { ["_data"]=> array(15) { ["id"]=> int(9673) ["email"]=> string(19) "ljradonjic@np.ac.rs" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6854) ["seq"]=> int(6) ["userGroupId"]=> int(116) ["country"]=> string(2) "RS" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(9) "Ljubivoje" ["hu_HU"]=> string(9) "Ljubivoje" } ["givenName"]=> array(2) { ["en_US"]=> string(8) "Radonjic" ["hu_HU"]=> string(8) "Radonjic" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(1) { ["en_US"]=> array(1) { [0]=> string(82) "digital labour platforms, online labour, labor markets, Southeast Europe, Gigmetar" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#872 (7) { ["_submissionFile"]=> NULL ["_data"]=> array(9) { ["submissionFileId"]=> int(34650) ["id"]=> int(5951) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(6854) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }
PDF
object(Publication)#858 (6) { ["_data"]=> array(28) { ["id"]=> int(6982) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2024-06-28" ["lastModified"]=> string(19) "2024-07-18 11:12:30" ["primaryContactId"]=> int(8637) ["sectionId"]=> int(17) ["seq"]=> int(7) ["submissionId"]=> int(6858) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(8811) "ANANNY, Mike – CRAWFORD, Kate (2018): Seeing without Knowing: Limitations of Transparency Ideal and Its Application to Algorithmic Accountability. New Media & Society, 20(3), 973–989. Online: https://doi.org/10.1177/1461444816676645 Delivery platform’s Induction Material (2022). Unpublished. ANDERSON, L. – WESTBERG, C. eds. (2016): Voices of Workable Futures. People Transforming Work in the Platform Economy: The Institute for the Future. BALL, Carolyn (2009): What is Transparency? Public Integrity, 11(4), 293–307. Online: https://doi.org/10.2753/PIN1099-9922110400 BOBILLIER CHAUMON, M.-E. (2021): Technologies emergentes et transformations digitales de l’activité: enjeux pour l’activité et la santé au travail. Psychologie de travail et des organisations, 27, 17–32. Online: https://doi.org/10.1016/j.pto.2021.01.002 BRIGHENTI, Andrea (2007): Visibility. A Category for the Social Sciences. Current Sociology, 55(3), 323–342. Online: https://doi.org/10.1177/0011392107076079 Cedefop (2020): Developing and Matching Skills in the Online Platform Economy: Findings on New Forms of Digital Work and Learning from Cedefop's CrowdLearn Study. Luxembourg: Publications Office. Cedefop reference series; No 116. Online: https://doi.org/10.2801/588297 CHRISTENSEN, Lars T. – CHENEY, George (2015): Peering into Transparency: Challenging Ideas, Proxies and Organizational Practices. Communication Theory, 25(1), 70–90. Online: https://doi.org/10.1111/comt.12052 DEJOURS, C. (1993): Intelligence pratique et sagesse pratique: deux dimensions méconnues du travail réel. Éducation Permanente, 116, 47–60. DENG, Xuefei – JOSHI, K. D. – GALLIERS, Robert D. (2016): The Duality of Empowerment and Marginalization in Microtask Crowdsourcing: Giving Voice to the Less Powerful through Value Sensitive Design. MIS Quarterly, 40(2), 279–302. Online: https://doi.org/10.25300/MISQ/2016/40.2.01 FLYVERBOM, Mikkel (2022): Overlit: Digital Architectures of Visibility. Organization Theory, 3(3). Online: https://doi.org/10.1177/26317877221090314 HARDGRAVE, Timothy J. – VAN DE VEN, Andrew (2017): Integrating Dialectical and Paradox Perspectives on Managing Contradictions in Organizations. Organization Studies, 38(3–4), 319–339. Online: https://doi.org/10.1177/0170840616640843 HARNESS, Delaney – GANESH, Shiv – STOHL, Cynthia (2022): Visibility Agents: Organizing Transparency in the Digital Era. New Media & Society, online first, November 26, 2022. Online: https://doi.org/10.1177/14614448221137816 KEMPER, Jakko – KOLKMAN, Daan (2018): Transparent to Whom? No Algorithmic Accountability Without a Critical Audience. Information, Communication & Society, 22(14), 2081–2096. Online: https://doi.org/10.1080/1369118X.2018.1477967 KORNBERGER, Martin – PFLUEGER, Dane – MOURITSEN, Jan (2017): Evaluative Infrastructures: Accounting for Platform Organization. Accounting, Organizations and Society, 60, 79–95. Online: https://doi.org/10.1016/j.aos.2017.05.002 LEONARDI, Paul – TREEM, Jeffrey (2020): Behavioral Visibility: A New Paradigm for Organization Studies in the Age of Digitization, Digitalization and Datafication. Organization Studies, 41(12), 1601–1625. Online: https://doi.org/10.1177/0170840620970728 LEWIS, M. W., – SMITH, W. K. (2014): Paradox as a metatheoretical perspective: Sharpening the Focus and Widening the Scope. The Journal of Applied Behavioral Science, 50(2), 127–149. Online: https://doi.org/10.1177/0021886314522322 MAZMANIAN, Melissa (2013): Avoiding the Trap of Constant Connectivity: When Congruent Frames Allow for Heterogeneous Practices. Academy of Management Journal, 56(5), 1225–1250. Online: https://doi.org/10.5465/amj.2010.0787 MAZMANIAN, Melissa – ORLIKOWSKI, Wanda – YATES, JoAnne (2013): The Autonomy Paradox: The Implications of Mobile Email Devices for Knowledge Professionals. Organization Science, 24(5), 1337–1357. Online: https://doi.org/10.1287/orsc.1120.0806 PICHAULT, François – MCKEOWN, Tui (2019): Autonomy at Work in the Gig Economy: Analysing Work Status, Work Content and Working Conditions of Independent Professionals. New Technology, Work and Employment, 34(1), 59–72. Online: https://doi.org/10.1111/ntwe.12132 PESOLE, A. – URZI BRANCATI, M. C. – FERNÁNDEZ-MACIAS, E. – BIAGI, F. – GONZALEZ VAZQUEZ, I. (2018): Platform Workers in Europe Evidence from the COLLEEM Survey. Luxembourg: Publications Office of the European Union. Online: https://doi.org/10.2760/742789 POWER, Michael (2022): Theorizing the Economy of Traces: From Audit Society to Surveillance Capitalism. Organization Theory, 3(3). Online: https://doi.org/10.1177/26317877211052296 RAHMAN, Hatim A. (2021): The Invisible Cage: Workers' Reactivity to Opaque Algorithmic Evaluations. Administrative Science Quarterly, 66(4), 945–988. Online: https://doi.org/10.1177/00018392211010118 SEPPÄNEN, L. – HASU, M. – KÄPYKANGAS, S. – POUTANEN, Seppo (2018): On-demand Work in Platform Economy: Implications for Sustainable Development. In BAGNARA, S. – TARTAGLIA, ALBOLINO, R. S. – ALEXANDER, T. – FUJITA, Y. (eds.): Proceedings of the 20th Congress of International Ergonomics Association (IEA 2018). Cham: Springer, 803–811. Online: https://doi.org/10.1007/978-3-319-96068-5_86 SEPPÄNEN, Laura – POUTANEN, Seppo – ROUVINEN, P. (2019): Millaista yrittäjyyttä alustatyö edistää? Esimerkkinä Upwork Suomessa [What kind of entrepreneurship does platform work enhance?]. Työpoliittinen aikakauskirja, (1), 20–28. SEPPÄNEN, Laura – POUTANEN, Seppo (2020): Cultural Transition in the Sharing Economy? Introducing Platform Work with Activity Concepts. In POUTANEN, S. – KOVALAINEN, A. – ROUVINEN, P. (eds.): Digital Work and the Platform Economy. Understanding Tasks, Skills and Capabilities in the New Era. New York and London: Routledge, 183–202. Online: https://doi.org/10.4324/9780429467929-10 SEPPÄNEN, Laura – KÄNSÄLÄ, M. – IMMONEN, J. – ALASOINI, T. (2022): Näkökulmia alustatyön reiluuteen. Reiluuden mallit alustatyössä -hankkeen loppuraportti. Tietoa työstä. [Perspectives into fairness of platform work. Final report of the Models of fairness in platform work–project]. SEPPÄNEN, Laura – TOIVIAINEN, Hanna – HASU, Mervi (2023): Workplace Learning for Fair Work on Digital Labour Platforms. In BOUND, Helen – EDWARDS, Anne – EVANS, Karen – CHIA, Arthur (eds.): Workplace Learning for Changing Social and Economic Circumstances. Routledge, 171–184. Online: https://doi.org/10.4324/9781003227946-14 STOHL, Cynthia – STOHL, Michael – LEONARDI, Paul (2016): Managing Opacity: Information Visibility and the Paradox of Transparency in the Digital Age. International Journal of Communication, 10(2016), 123–137. SUNDARARAJAN, Arun (2016): The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism. Cambridge, MA: The MIT Press. TADELIS, Steven (2016): Reputation and Feedback Systems in Online Platform Markets. Annual Review of Economics, 8(3), 21–40. Online: https://doi.org/10.1146/annurev-economics-080315-015325 VAIRIMAA, Reetta (2023): Digitalisaatiosta toivottiin tuottavuusloikkaa – miksi asiantuntijoiden aika menee tietojärjestelmien kanssa tappelemiseen? [An efficiency leap was expected from digitalization – why experts’ time is wasted to fight with information systems?] Helsingin Yliopisto, 4/2023. Online: https://www.helsinki.fi/fi/uutiset/digitalisaatio/digitalisaatiosta-toivottiin-tuottavuusloikkaa-miksi-asiantuntijoiden-aika-menee-tietojarjestelmien-kanssa-tappelemiseen VALLAS, Steven – SCHOR, Juliet B. (2020): What Do Platforms Do? Understanding the Gig Economy. Annual Review of Sociology, 46, 273–294. Online: https://doi.org/10.1146/annurev-soc-121919-054857 VAN DOORN, N. – BADGER, A. (2021): Dual Value Production as Key to the Gig Economy Puzzle. In MEIJERINK, J. – JANSEN, G. – V. DASKALOVA (eds.): Platform Economy Puzzles. A Multidisciplinary Perspective on Gig Work. Cheltenham: Edward Elgar, 123–139. Online: https://doi.org/10.4337/9781839100284.00015 WOOD, Alex J. (2021): Algorithmic Management. Consequences for Work Organisation and Working Conditions. Seville: European Commission. WOOD, Alex J. – GRAHAM, Mark – LEHDONVIRTA, Vili – HJORTH, Isis (2019): Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy. Work, Employment and Society, 33(1), 56–75. Online: https://doi.org/10.1177/0950017018785616 WURHOFER, Daniela – MENEWEGER, Thomas – FUCHSBERGER, Verena – TSCHELIGI, Manfred (2018): Reflections on Operators’ and Maintenance Engineers’ Experiences of Smart Factories. In Proceedings of the 2018 ACM Conference on Supporting Groupwork, Sanibel Island, USA. Online: https://doi.org/10.1145/3148330.3148349" ["copyrightYear"]=> int(2024) ["issueId"]=> int(594) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(7) "173-192" ["pub-id::doi"]=> string(21) "10.32575/ppb.2024.1.8" ["abstract"]=> array(2) { ["en_US"]=> string(933) "

Digital technologies can considerably increase the visibility of people’s behaviours and activities, and therefore researchers should pay more attention to visibility and opaqueness in organisations. This paper focuses on visibility in terms of the information given or mediated to workers. The aim of this paper is to examine consequences of visibility for workers who carry out work tasks through digital labour platforms. The research will focus on how visibility or opaqueness in practice promotes or hinders workers’ capacity to act and to make informed choices in their work. The visibility paradoxes of connectivity, performance and transparency are used as methodical lenses.
The same platform operations can have both empowering and marginalising consequences for workers. While labour platforms continuously improve visibility to workers, they may also hide, inadvertently or intentionally, key information.

" ["hu_HU"]=> string(697) "

Leonardi & Treem (2020:1602) argue that while digitization, digitalization and datafication afford a massive increase in the behavioral visibility of actors, academic research needs to better examine, how transparency and visibility are performed, managed and evaluated in organizations, not forgetting the important role of connectivity in these processes. Visibility can create digital trust between strangers. It is of interest, how algorithmic systems mediating visibility become embedded in the networks of people and existing systems that make use of them, and with what consequences. For understanding the processes of visibility, qualitative and ethnographic research is needed.

" } ["copyrightHolder"]=> array(1) { ["en_US"]=> string(14) "Laura Seppanen" } ["title"]=> array(2) { ["en_US"]=> string(66) "The Consequences of Visibility and Opaqueness for Platform Workers" ["hu_HU"]=> string(51) "Consequences of (in)visibility for platform workers" } ["locale"]=> string(5) "en_US" ["authors"]=> array(1) { [0]=> object(Author)#883 (6) { ["_data"]=> array(15) { ["id"]=> int(8637) ["email"]=> string(21) "laura.seppanen@ttl.fi" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6982) ["seq"]=> int(7) ["userGroupId"]=> int(116) ["country"]=> string(2) "FI" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(5) "Laura" ["hu_HU"]=> string(5) "Laura" } ["givenName"]=> array(2) { ["en_US"]=> string(8) "Seppanen" ["hu_HU"]=> string(8) "Seppanen" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(1) { ["en_US"]=> array(7) { [0]=> string(16) "autonomy paradox" [1]=> string(23) "digital labour platform" [2]=> string(19) "performance paradox" [3]=> string(16) "platform workers" [4]=> string(20) "transparency paradox" [5]=> string(10) "visibility" [6]=> string(18) "visibility paradox" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#879 (7) { ["_submissionFile"]=> NULL ["_data"]=> array(9) { ["submissionFileId"]=> int(34651) ["id"]=> int(5952) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(6982) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }
PDF
object(Publication)#867 (6) { ["_data"]=> array(29) { ["id"]=> int(7092) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2024-06-28" ["lastModified"]=> string(19) "2024-07-18 11:13:33" ["primaryContactId"]=> int(8797) ["sectionId"]=> int(17) ["seq"]=> int(8) ["submissionId"]=> int(6968) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(7385) "ALASOINI, Tuomo – IMMONEN, Jere – SEPPÄNEN, Laura – KÄNSÄLÄ, Marja (2023): Platform Workers and Digital Agency: Making Out on Three Types of Labor Platforms. Frontiers of Sociology, 8(2023). Online: https://doi.org/10.3389/fsoc.2023.1063613 BRONOWICKA, Joanna – IVANOVA, Mirela (2020): Resisting the Algorithmic Boss: Guessing, Gaming, Reframing and Contesting Rules in App-Based Management. SSRN Electronic Journal. Online: https://doi.org/10.2139/ssrn.3624087 BURAWOY, Michael (1982): Manufacturing Consent: Changes in the Labor Process under Monopoly Capitalism. Chicago, IL: University of Chicago Press. Online: https://doi.org/10.7208/chicago/9780226217710.001.0001 CANT, Callum (2019): Riding for Deliveroo: Resistance in the New Economy. Cambridge: Polity. CHESTA, Riccardo – ZAMPONI, Lorenzo – CACIAGLI, Carlotta (2019): Labour Activism and Social Movement Unionism in the Gig Economy. Food Delivery Workers’ Struggles in Italy. PACO, 12(3), 819–844. Online: https://doi.org/10.1285/i20356609v12i3p819 DE STEFANO, Valerio (2015): The Rise of the ‘Just-in-Time Workforce’: On-Demand Work, Crowd Work and Labour Protection in the ‘Gig-Economy’. SSRN Electronic Journal. Online: https://doi.org/10.2139/ssrn.2682602 DELLA PORTA, Donatella – CHESTA, Riccardo Emilio – CINI, Lorenzo (2022): Mobilizing against the Odds. Solidarity in Action in the Platform Economy. Berliner Journal Für Soziologie, 32(2), 213–241. Online: https://doi.org/10.1007/s11609-022-00471-z DRAHOKOUPIL, Jan – KAHANCOVÁ, Marta – MESZMANN, Tibor T. (2022): Falling through the Cracks – Gig Economy and Platform Work in Central and Eastern Europe. In NESS, Immanuel (ed.): The Routledge Handbook of the Gig Economy. London: Routledge, 309–323. Online: https://doi.org/10.4324/9781003161875-25 ENGLERT, Sai – GRAHAM, Mark – FREDMAN, Sandra – DU TOIT, Darcy – BADGER, Adam – HEEKS, Richard – VAN BELLE, Jean-Paul (2021): Workers, Platforms and the State: The Struggle over Digital Labour Platform Regulation. In DRAHOKOUPIL, Jan (ed.): A Modern Guide To Labour and the Platform Economy. Cheltenham: Edward Elgar, 162–176. Online: https://doi.org/10.4337/9781788975100.00020 GALIÈRE, Sophia (2020): When Food-Delivery Platform Workers Consent to Algorithmic Management: A Foucauldian Perspective. New Technology, Work and Employment, 35(3), 357–370. Online: https://doi.org/10.1111/ntwe.12177 KAHANCOVÁ, Marta, MESZMANN, Tibor T. – SEDLÁKOVÁ, Mária (2020): Precarization via Digitalization? Work Arrangements in the On-Demand Platform Economy in Hungary and Slovakia. Frontiers in Sociology, 5. Online: https://doi.org/10.3389/fsoc.2020.00003 MAKÓ, Csaba – ILLÉSSY, Miklós – NOSRATABADI, Saeed (2020): Emerging Platform Work in Europe: Hungary in Cross-country Comparison. European Journal of Workplace Innovation, 5(2), 147–172. Online: https://doi.org/10.46364/EJWI.V5I2.759 MAKÓ, Csaba – ILLÉSSY, Miklós – PAP, József (2021a): National Context: Hungary. In MONIZ, A. B. – BOAVIDA, N. – MAKÓ, Cs. – KRINGS, B. J. – DE MIGUEL, P. S. (eds.): Digital Labour Platforms: Representing Workers in Europe. Vila Nova de Famalicão: Edições Húmus – CICS.NOVA, 23–28. Online: https://doi.org/10.34619/rwrm-3uun MAKÓ, Csaba – ILLÉSSY, Miklós – PAP, József (2021b): Wolt: A High Growth Platform in the Delivery Economy in Hungary. In MONIZ, A. B. – BOAVIDA, N. – MAKÓ, Cs. – KRINGS, B. J. – DE MIGUEL, P. S. (eds.): Digital Labour Platforms: Representing Workers in Europe. Vila Nova de Famalicão: Edições Húmus – CICS.NOVA, 95–110. Online: https://doi.org/10.34619/rwrm-3uun MAKÓ, Csaba – ILLÉSSY, Miklós – PAP, József and NOSRATABADI, Saeed (2022): Emerging Platform Work in the Context of the Regulatory Loophole (The Uber Fiasco in Hungary). Journal of Labor and Society, 1(aop), 1–22. Online: https://doi.org/10.1163/24714607-bja10054 MARCUSE, Herbert (1991): One-Dimensional Man: Studies in the Ideology of Advanced Industrial Society. 2nd Edition. Boston: Beacon Press. MUELLER, Gavin (2021): Breaking Things at Work: The Luddites Are Right About Why You Hate Your Job. London: Verso. NAGY, Klára (2023): Body and Mind. Reframing Labour Exploitation and Risk as a Sport among Platform Workers. (The Case of the Food Delivery Sector in Budapest). Pro Publico Bono, under release. Online: https://doi.org/10.32575/ppb.2024.1.10 PERRIG, Luca (2021): Manufacturing Consent in the Gig Economy. In MOORE, Phoebe V. – WOODCOCK, Jamie (eds.): Augmented Exploitation. London: Pluto Press, 75–86. Online: https://doi.org/10.2307/j.ctv1h0nv3d.12 PURCELL, Christina – BROOK, Paul (2020): At Least I’m My Own Boss! Explaining Consent, Coercion and Resistance in Platform Work. Work, Employment and Society, 36(3), 391–406. Online: https://doi.org/10.1177/0950017020952661 RÁCZ-ANTAL, Ildikó (2022): A digitalizáció hatása a munkajog egyes alapintézményeire. Jog és Állam, 40. Budapest: Károli Gáspár Református Egyetem Állam- és Jogtudományi Kar Online: https://ajk.kre.hu/images/doc2022/pr/A_digitalizacio_hatasa_a_munkajog_egyes_alapintezmenyeire.pdf ROSENBLAT, Alex – STARK, Luke (2016): Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers. International Journal of Communication, 10, 3758–3784. Online: https://doi.org/10.2139/ssrn.2686227 STAAB, Philipp – NACHTWEY, Oliver (2016) Market and Labour Control in Digital Capitalism. tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society, 14(2), 457–474. Online: https://doi.org/10.31269/triplec.v14i2.755 STANDING, Guy (2011): The Precariat. London: Bloomsbury. Online: https://doi.org/10.5040/9781849664554 SZÉPE, András (2012): Prekariátus: Miért Pont Most És Miért Pont Itt? Fordulat, 19, 10–27. TASSINARI, Arianna – MACCARRONE, Vincenzo (2020): Riders on the Storm: Workplace Solidarity among Gig Economy Couriers in Italy and the UK. Work, Employment and Society, 34(1), 35–54. Online: https://doi.org/10.1177/0950017019862954 VAN DOORN, Niels – CHEN, Julie Yujie (2021): Odds Stacked against Workers: Datafied Gamification on Chinese and American Food Delivery Platforms. Socio-Economic Review, 19(4), 1345–1367. Online: https://doi.org/10.1093/ser/mwab028 VANDAELE, Kurt (2021): Collective Resistance and Organizational Creativity amongst Europe’s Platform Workers: A New Power in the Labour Movement? In HAIDAR, Julieta – KEUNE, Maarten (eds.): Work and Labour Relations in Global Platform Capitalism. Cheltenham: Edward Elgar, 206–235. Online: https://doi.org/10.4337/9781802205138.00019 VEEN, Alex – BARRATT, Tom – GOODS, Caleb (2020): Platform-Capital’s ‘App-Etite’ for Control: A Labour Process Analysis of Food-Delivery Work in Australia. Work, Employment and Society, 34(3), 388–406. Online: https://doi.org/10.1177/0950017019836911 WOODCOCK, Jamie (2021): Understanding Platform Resistance. In The Fight against Platform Capitalism: An Inquiry into the Global Struggles of the Gig Economy. Westminster: University of Westminster Press, 67–83. Online: https://doi.org/10.16997/book51.e WOODCOCK, Jamie – JOHNSON, Mark R. (2018): Gamification: What It Is, and How to Fight It. The Sociological Review, 66(3), 542–558. Online: https://doi.org/10.1177/0038026117728620" ["copyrightYear"]=> int(2024) ["issueId"]=> int(594) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(7) "193-214" ["pub-id::doi"]=> string(21) "10.32575/ppb.2024.1.9" ["abstract"]=> array(2) { ["en_US"]=> string(1092) "

This research examines the work organisation of the Foodpanda food delivery firm and the experiences of the bicycle couriers who work for it, particularly their attitudes to the algorithmic management of their work. The focus of the inquiry is the gamification of work, both from-above and from-below. Gamification from-above is constructed by the management. Taking part in the games can be a source of pride and satisfaction, but also of addiction and self-exploitation. Gamification from-below includes all kinds of “games” that the couriers initiate. These can be different strategies to earn more money, save energy or sabotage the labour process. The study shows the connection between games and the formation of consent and resistance among the couriers. The analysis differentiates between the games of making do and making out. Games of making do usually bring about consent, as they stay within the boundaries set by the management. In contrast, making out goes against managerial interest and gives agency to the couriers, thus it has the potential to foster resistance.

" ["hu_HU"]=> string(1692) "

The research examines the work organization of Foodpanda and the bicycle couriers’ experiences and attitudes regarding the algorithmic management of their work. The focus of the inquiry is the gamification of the work, from-above and from-below. In the first case, gamification is created from the side of the management, while in the second, games are initiated by workers.

Gamification from above consists of the gambling-like work process, the ranking of the couriers and the bonuses offered for completing “challenges” during work. The research found that taking part in the games can cause addiction and self-exploitation among couriers. Furthermore, successful participation in the game leads to pride and recognition from other workers. Gamification from below includes all kinds of “games” that the couriers initiate. These can be different strategies to earn more, while sparing energy; small sabotages of the application and bets among one another.

The study shows the connection between games and the formation of consent among the couriers. The findings conclude that by taking part in the games from-above, the couriers must accept the rules and the logic of the work organization. Furthermore, the games give space for relative satisfaction with one’s work. Therefore, the games from-above contribute to the formation of consent to the algorithmic work management. On the other hand, some games from-below give agency to the couriers, thus have the potential to advocate resistance. Nevertheless, the research found that the majority of games from-below (as for now) do not cause harm to the interest of the company.

" } ["copyrightHolder"]=> array(1) { ["en_US"]=> string(14) "Ürmössy Anna" } ["subtitle"]=> array(2) { ["en_US"]=> string(110) "The Role of Gamification in Algorithmic Management tf The Work Process (The Case of Food-Couriers in Budapest)" ["hu_HU"]=> string(37) "The Case of Food-couriers in Budapest" } ["title"]=> array(2) { ["en_US"]=> string(23) "Consent and Resistance " ["hu_HU"]=> string(78) "Consent or Resistance? The role of Gamification in Algorithmic Work Management" } ["locale"]=> string(5) "en_US" ["authors"]=> array(1) { [0]=> object(Author)#887 (6) { ["_data"]=> array(14) { ["id"]=> int(8797) ["email"]=> string(22) "anna.urmossy@gmail.com" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(7092) ["seq"]=> int(8) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(54) "Eötvös Loránd University Faculty of Social Sciences" ["hu_HU"]=> string(39) "Eötvös Loránd Tudományegyetem TÁTK" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(9) "Ürmössy" ["hu_HU"]=> string(9) "Ürmössy" } ["givenName"]=> array(2) { ["en_US"]=> string(4) "Anna" ["hu_HU"]=> string(4) "Anna" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(2) { ["hu_HU"]=> array(1) { [0]=> string(105) "gig-economy, platform work, algorithmic work management, food delivery, gamification, consent, resistance" } ["en_US"]=> array(6) { [0]=> string(16) "bicycle couriers" [1]=> string(18) "digital capitalism" [2]=> string(13) "food delivery" [3]=> string(11) "gig economy" [4]=> string(10) "making out" [5]=> string(13) "platform work" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#882 (7) { ["_submissionFile"]=> NULL ["_data"]=> array(9) { ["submissionFileId"]=> int(34652) ["id"]=> int(5953) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(7092) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }
PDF
object(Publication)#878 (6) { ["_data"]=> array(29) { ["id"]=> int(6848) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2024-06-28" ["lastModified"]=> string(19) "2024-07-18 11:14:21" ["primaryContactId"]=> int(8456) ["sectionId"]=> int(17) ["seq"]=> int(9) ["submissionId"]=> int(6724) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(6763) "BIRÓ, Attila (2022): 800 ezret is kaszálhat egy Wolt futár: rengeteg embert keresnek a nagyvárosokban. Pénzcentrum, 8 March 2021. Online: https://www.penzcentrum.hu/vallalkozas/20210308/800-ezret-is-kaszalhat-egy-wolt-futar-rengeteg-embert-keresnek-a-nagyvarosokban-1112028 BLAKE, Andrew (1996): The Body Language: The Meaning of Modern Sport. London: Lawrence & Wishart. CANT, Callum (2019): Riding for Deliveroo: resistance in the new economy. Polity. CHOE, Sang-Hun (2020): Delivery Workers in South Korea Say They’re Dying of ‘Overwork’. The New York Times, 15 December 2020. Online: https://www.nytimes.com/2020/12/15/world/asia/korea-couriers-dead-overwork.html CSORDAS, Thomas (1994): The Body as Representation and Being-in-the-World. In CSORDAS, Thomas (ed.): Embodiment and Experience. Cambridge: Cambridge University Press, 1–26. CSORDAS, Thomas (2002): Body/Meaning/Healing. New York: Palgrave Macmillan. Online: https://doi.org/10.1007/978-1-137-08286-2 DEPAUW, Karen P. (1997): The (In)visibility of Disability: Cultural Contexts and “Sporting Bodies”. Quest, 49(4), 416–430. Online: https://doi.org/10.1080/00336297.1997.10484258 FOUCAULT, Michel (2012): Discipline and Punish: The Birth of the Prison. New York: Vintage Books. FREYTAS-TAMURA, Kimiko (2020): Food Delivery Apps are Booming. Their Workers are often Struggling. The New York Times, 30 May 2022. Online: https://www.nytimes.com/2020/11/30/nyregion/bike-delivery-workers-covid-pandemic.html?action=click&module=RelatedLinks&pgtype=Article GYÜKERI, Mercédesz (2017): Az éhes vásárlóval nem jó ujjat húzni – a NetPincér-sztori. HVG, 4 April 2017. Online: https://hvg.hu/kkv/20170404_netpincer_cegportre_ekereskedelem_szorad_gabor HARAWAY, Donna (1990): Investment Strategies for the Evolving Portfolio of Primate Females. In JACOBUS, Mary – FOX KELLER, Evelyn – SUTTLEWORTH, Sally (eds.): Body/Politics: Women and the Discourses of Science. New York: Routledge, 139–162. HARVEY, David (2007): The Condition of Postmodernity: An Inquiry into the Origins of Cultural Change. Cambridge, MA: Blackwell. HOWE, P. David (2011): Sporting Bodies: Sensuous, Lived and Impaired. In MASCIA-LEES, Frances E. (ed.): A Companion to the Anthropology of the Body and Embodiment. Wiley-Blackwell, 276–291. Online: https://doi.org/10.1002/9781444340488.ch15 JACKSON, Jean E. (2011): Pain and Bodies. In MASCIA-LEES, Frances E. (ed.): A Companion to the Anthropology of the Body and Embodiment. Wiley-Blackwell, 370–387. Online: https://doi.org/10.1002/9781444340488.ch21 KISS, Soma Ábrahám (2021): “Nem vállalkozásod van, hanem egy bringád vagy egy Suzukid” – a kényszervállalkozás valósága. Mérce, 4 May 2021. Online: https://merce.hu/2021/05/04/nem-vallalkozasod-van-hanem-egy-bringad-vagy-egy-suzukid-a-kenyszervallalkozas-valosaga/ KUČINAC, Dunja (2021): Gig Work in CEE's Platform Economy: Delivery Drivers in Croatia. Lefteast, 5 May 2021. Online: https://lefteast.org/gig-work-cee-croatian-delivery-drivers MINTZ, Sidney Wilfred (2018): Time, Sugar, and Sweetness. In COUNIHAN, Carole – VAN ESTERIK, Penny – JULIER, Alice (eds.): Food and Culture. Routledge, 401–413. Online: https://doi.org/10.4324/9781315680347-28 PÁLÚR, Krisztina (2020): A világjárvány miatt már az ételrendelés sem lesz ugyanaz. Index, 15 March 2020. Online: https://index.hu/kultur/eletmod/2020/03/15/etelrendeles_hazhozszallitas_karanten/ PIASNA, Agnieszka – DRAHOKOUPIL, Jan (2021): Flexibility Unbound: Understanding the Heterogeneity of Preferences among Food Delivery Platform Workers. Socio-Economic Review, 19(4), 1397–1419. Online: https://doi.org/10.1093/ser/mwab029 PIASNA, Agnieszka – ZWYSEN, Wouter – DRAHOKOUPIL, Jan (2022): The Platform Economy in Europe: Results from the Second ETUI Internet and Platform Work Survey (IPWS). ETUI Research Paper-Working Paper. Online: https://doi.org/10.2139/ssrn.4042629 POPAN, Cosmin (2022): The Gig Economy's Corporate Crime Problem. Lefteast, 9 May 2022. Online: https://lefteast.org/gig-economy-corporate-crime-problem/ PULIGNANO, Valeria – PIASNA, Agnieszka (2021): The ‘Freedom’ to Work for Nothing. Social Europe, 8 December 2021. Online: https://socialeurope.eu/the-freedom-to-work-for-nothing RANI, Uma – KUMAR DHIR, Rishabh – FURRER, Marianne – GŐBEL, Nóra – MORAITI, Angeliki – COONEY, Sean – CODDOU, Alberto (2021): World Employment and Social Outlook: The Role of Digital Labour Platforms in Transforming the World of Work. Geneva: International Labour Organization. Online: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_771749.pdf REID, Carlton (2018): Confessions of a Deliveroo Rider: Get Fit by Delivering Fast Food. The Guardian, 8 January 2018. Online: https://www.theguardian.com/environment/bike-blog/2018/jan/08/confessions-of-a-deliveroo-rider-get-fit-by-delivering-fast-food REID, Carlton (2019): Bicycle Courier is the Job that Burns Most Fat, Finds Fitness Guru. Forbes, 14 January 2019. Online: https://www.forbes.com/sites/carltonreid/2019/01/14/bicycle-courier-is-the-job-that-burns-most-fat-finds-fitness-guru/?sh=afb58ef2ee4a SCHOLZ, Trebor (2017): Uberworked and Underpaid: How Workers are Disrupting the Digital Economy. John Wiley & Sons. STEWART, Andrew – STANFORD, Jim (2017): Regulating Work in the Gig Economy: What are the Options? The Economic and Labour Relations Review, 28(3), 420–437. Online: https://doi.org/10.1177/1035304617722461 TÓTH, Katalin (2019): “I love Budapest. I bike Budapest?” Urbaner Radverkehr in der ungarischen Hauptstadt, 1980–2014. Vandenhoeck & Ruprecht. Online: https://doi.org/10.13109/9783666310720 VALLAS, Steven – SCHOR, Juliet (2020): What Do Platforms Do? Understanding the Gig Economy. Annual Review of Sociology, 46, 273–294. Online: https://doi.org/10.1146/annurev-soc-121919-054857 VAN DOORN, Niels (2017): Platform Labor: On the Gendered and Racialized Exploitation of Low-Income Service Work in the ‘On-Demand’ Economy. Information, Communication & Society, 20(6), 898–914. Online: https://doi.org/10.1080/1369118X.2017.1294194 VERES, Dóra (2020): Rengeteg melóst keresnek most országszerte: 700 ezres nettó, diploma nem kell. Pénzcentrum, 7 April 2020. Online: https://www.penzcentrum.hu/egeszseg/20200407/rengeteg-melost-keresnek-most-orszagszerte-700-ezres-netto-diploma-nem-kell-1092314 WACQUANT, Loïc (2004): Body & Soul: Notebooks of an Apprentice Boxer. New York: Oxford University Press. ZHAO, Lily (2021): Suicide Attempt by Food Delivery Worker in China Exposes Exploitative Working Conditions. World Socialist Web Site, 21 January 2021. Online: https://www.wsws.org/en/articles/2021/01/22/chin-j22.html" ["copyrightYear"]=> int(2024) ["issueId"]=> int(594) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(7) "215-236" ["pub-id::doi"]=> string(22) "10.32575/ppb.2024.1.10" ["abstract"]=> array(2) { ["en_US"]=> string(1198) "

Cycling food couriers in Hungary tend to normalise and justify for themselves the precarious gig working conditions as a sports activity. To understand the blurring between sport and work, I carried out participant observation, conducted semi-structured interviews and discourse analysis. I worked as a bicycle courier in Budapest in July and August 2021. The successful boom of the cycling-based food delivery platforms depends on the extraction of bodily resources. Food delivery companies create new frontiers as they frame labour as challenging cardio activity. The riders embrace the idea that they get paid for training their body, which activity is otherwise expensive and tiring. The workers utilise their knowledge from their past sporting activities about nutrition and pain relief to increase their workload. Sporting rivalry and boasting of results are active features of the courier community. Although my interviewees proudly claimed themselves entrepreneurs, the body experiences reveal the cleavage between gig wage labour and idealised entrepreneurship. The pain and dangers of urban cycling work highlight the unequal relationship and make couriers critical of the company.

" ["hu_HU"]=> string(2384) "

Az ételkiszállítás a platformgazdaság egyik leglátványosabb ágazata, amely az elmúlt években több ezer embert szerződtetett Budapesten. Ez a kutatás azt vizsgálja, hogy a kerékpáros ételkiszállítók hogyan fogadják el, normalizálják és igazolják a bizonytalan munkakörülményeket, a munkaerő kizsákmányolását és a kockázatot. Az ételkiszállítási ágazat kritizálásának és mégis elfogadásának paradoxona aktív jellemzője a magyarországi futárközösségnek. A futárok vezető Facebook-csoportja és az általam készített előzetes interjúk tele vannak a futárcégekkel szembeni kemény kritikákkal. A futárok azonban továbbra is szerződésben állnak ezekkel a cégekkel, és büszkén vállalják a közös futáridentitást. Hogyan fogadják el, normalizálják és igazolják a magyarországi ételfutárok a bizonytalan munkakörülményeket, a munkaerő kizsákmányolását és a kockázatot?
A magyarországi kerékpáros ételfutárok hajlamosak sporttevékenységként normalizálni és igazolni maguk számára a bizonytalan munkakörülményeket. A sport és a munka közötti elmosódás megértése érdekében résztvevő megfigyelést végeztem, félig strukturált interjúkat készítettem és diskurzuselemzést végeztem. Kerékpáros futárként dolgoztam Budapesten 2021 júliusában és augusztusában.
A kerékpáros ételkézbesítő platformok sikeres fellendülése a testi erőforrások kitermelésétől függ. Az ételkiszállító cégek új határokat teremtenek, a munkát kihívást jelentő kardiótevékenységként keretezik. A kerékpárosok elfogadják az ideát, hogy fizetést kapnak a testük edzéséért, amely tevékenység egyébként költséges és fárasztó. A munkások a korábbi sporttevékenységekből származó táplálkozási és fájdalomcsillapítási ismereteiket használják fel a munkaterhelés növelésére. A sportversenyzés és az eredményekkel való dicsekvés aktív jellemzője a futóközösségnek.
Bár interjúalanyaim büszkén állították, hogy vállalkozók, testük tapasztalatai a platform munka és az idealizált vállalkozói lét közötti szakadékot mutatják. A városi kerékpáros munka fájdalmai és veszélyei rávilágítanak az egyenlőtlen viszonyra, és kritikussá teszik a futárokat a vállalkozással szemben.

" } ["copyrightHolder"]=> array(1) { ["en_US"]=> string(11) "Nagy Klára" } ["subtitle"]=> array(2) { ["en_US"]=> string(122) "Reframing Labour Exploitation and Risk as a Sport Among Platform Workers. The Case of the Food Delivery Sector in Budapest" ["hu_HU"]=> string(123) "Reframing labor exploitation and risk as a sport among platform workers. (The case of the food delivery sector in Budapest)" } ["title"]=> array(2) { ["en_US"]=> string(13) "Body and Mind" ["hu_HU"]=> string(13) "Body and Mind" } ["locale"]=> string(5) "en_US" ["authors"]=> array(1) { [0]=> object(Author)#876 (6) { ["_data"]=> array(15) { ["id"]=> int(8456) ["email"]=> string(30) "nagy.klara@periferiakozpont.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6848) ["seq"]=> int(9) ["userGroupId"]=> int(116) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(70) "a:1:{s:5:"en_US";s:44:"Periféria Public Policy and Research Center";}" ["hu_HU"]=> string(70) "a:1:{s:5:"en_US";s:44:"Periféria Public Policy and Research Center";}" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(4) "Nagy" ["hu_HU"]=> string(4) "Nagy" } ["givenName"]=> array(2) { ["en_US"]=> string(6) "Klára" ["hu_HU"]=> string(6) "Klára" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(2) { ["hu_HU"]=> array(1) { [0]=> string(7) "English" } ["en_US"]=> array(5) { [0]=> string(16) "platform economy" [1]=> string(8) "gig work" [2]=> string(13) "food delivery" [3]=> string(18) "labor exploitation" [4]=> string(14) "embodied labor" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#889 (7) { ["_submissionFile"]=> NULL ["_data"]=> array(9) { ["submissionFileId"]=> int(34653) ["id"]=> int(5954) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(6848) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }
PDF

Outlook

object(Publication)#763 (6) { ["_data"]=> array(29) { ["id"]=> int(6511) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2024-06-28" ["lastModified"]=> string(19) "2024-07-18 11:15:06" ["primaryContactId"]=> int(8026) ["sectionId"]=> int(110) ["seq"]=> int(1) ["submissionId"]=> int(6387) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(3866) "BALKIN, Jack M. (2016): Information Fiduciaries and the First Amendment. University of California Davis Law Review, 49(4), 1185–1234. BALKIN, Jack M. (2020): The Fiduciary Model of Privacy. Harvard Law Review Forum, 134(11), 11–33. Online: https://harvardlawreview.org/wp-content/uploads/2020/10/134-Harv.-L.-Rev.-F.-11.pdf BUTTARELLI, Giovanni (2019): Privacy 2030: A New Vision for Europe. Online: https://iapp.org/media/pdf/resource_center/giovanni_manifesto.pdf DPC (2023): Data Protection Commission announces conclusion of two inquiries into Meta Ireland. 4 January 2023. Online: https://www.dataprotection.ie/en/news-media/data-protection-commission-announces-conclusion-two-inquiries-meta-ireland EDPB opinion 39/2021 (December 14, 2021). Online: https://edpb.europa.eu/system/files/2022-01/edpb_opinion_202139_article_582g_gdpr_en.pdf FARRELL, Maria (2019): Afterword: A Cage Went in Search of A Bird. In BUTTARELLI, Giovanni: Privacy 2030: A New Vision for Europe. [s. l.]: IAPP, 35–36. Online: https://iapp.org/media/pdf/resource_center/giovanni_manifesto.pdf HARARI, Yuval Noah (2014): Sapiens. London: Vintage Books. HARARI, Yuval Noah (2017): Homo Deus. London: Vintage Books. JAYARAM, Malavika (2019): Afterword: The Future is Already Distributed – It’s Not Evenly Just. In BUTTARELLI, Giovanni: Privacy 2030: A New Vision for Europe. [s. l.]: IAPP, 31–32. Online: https://iapp.org/media/pdf/resource_center/giovanni_manifesto.pdf KOLBERT, Elizabeth (2015): The Sixth Extinction. London – New Delhi – New York – Sydney: Bloomsbury. NAIH (2021): A Nemzeti Adatvédelmi és Információszabadság Hatóság Beszámolója a 2021. évi tevékenységéről. Budapest: Nemzeti Adatvédelmi és Információszabadság Hatóság. Online: https://naih.hu/eves-beszamolok?download=507:naih-beszamolo-a-2021-evi-tevekenysegrol NAIH equal opportunities policy (June 15, 2022) PANETTA, Rocco (2019): Afterword: Privacy 2030: To Give Humans a Chance. In BUTTARELLI, Giovanni: Privacy 2030: A New Vision for Europe. [s. l.]: IAPP, 37–40. Online: https://iapp.org/media/pdf/resource_center/giovanni_manifesto.pdf PATYI, András (2017): A közigazgatási működés jogi alapjai. Budapest: Dialóg Campus. POLONETSKY, Jules (2019): Afterword: A Mission Greater Than Compliance. In BUTTARELLI, Giovanni: Privacy 2030: A New Vision for Europe. [s. l.]: IAPP, 33–34. Online: https://iapp.org/media/pdf/resource_center/giovanni_manifesto.pdf RODOTÁ, Stefano (2004): Privacy, libertà, dignità. Discorso conclusivo della Conferenza internazionale sulla protezione dei dati. Online: https://www.privacy.it/archivio/rodo20040916.html RODRIK, Dani (2012): The Globalization Paradox. Oxford: Oxford University Press. ROTENBERG, Marc (2019): Afterword: The Future of Privacy and a Vibrant Democracy. In BUTTARELLI, Giovanni: Privacy 2030: A New Vision for Europe. [s. l.]: IAPP, 29–30. Online: https://iapp.org/media/pdf/resource_center/giovanni_manifesto.pdf SARTORI, Giovanni (1987): Theory of Democracy Revisited I–II Chatham, NJ: Chatham House. SZABÓ, Endre Győző (2022): A védelmi lépcső elmélete. Budapest: Ludovika. ZUBOFF, Shoshana (2019): Afterword: Many Facets of the Same Diamond. In BUTTARELLI, Giovanni: Privacy 2030: A New Vision for Europe. [s. l.]: IAPP, 41–42. Online: https://iapp.org/media/pdf/resource_center/giovanni_manifesto.pdf Constitutional Court of Hungary’s decisions 2/2019 (III. 5.) AB decision 3110/2022 (III. 23.) AB decision Court decisions Kúria’s judgement No. Kfv.II.37.001/2021/6. Budapest-Capital Regional Court's judgement No. 105.K.706.125/2020/12. NAIH decisions NAIH/2020/974/4. Online: https://www.naih.hu/files/NAIH-2020-974-hatarozat.pdf NAIH-85-3/2022. Online: https://www.naih.hu/hatarozatok-vegzesek/file/517-mesterseges-intelligencia-alkalmazasanak-adatvedelmi-kerdesei " ["copyrightYear"]=> int(2024) ["issueId"]=> int(594) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(7) "237-261" ["pub-id::doi"]=> string(22) "10.32575/ppb.2024.1.11" ["abstract"]=> array(2) { ["en_US"]=> string(982) "

European Data Protection Supervisor (EDPS) Giovanni Buttarelli’s posthumous manifesto, Privacy 2030: A New Vision for Europe, places data protection in a global context. Competition and data protection authorities within the EU cooperate and share information about their official inquiries. If properly enforced, the GDPR may be an effective tool of transparent data processing in the EU, and can serve as a model for the rest of the world. Enforcement is the duty of Member States’ DPAs, therefore, it may be worth analysing Buttarelli’s views in relation to the issues currently facing Hungarian data protection regulation. The paper critically presents Buttarelli’s main views, while discussing them in relation to Hungarian public administration through a specific legal case. As a result of the comparative analysis, it can be concluded that by enhancing the data protection culture and its administrative enforcement, our personal data can be better protected.

" ["hu_HU"]=> string(1133) "

European Data Protection Supervisor (EDPS) Giovanni Buttarelli’s posthumous manifesto, „Privacy 2030: A New Vision for Europe”, places data protection in a global context. In his view, a digital underclass has emerged with members who have no access to the necessary informations to understand the logic of the algorithmic decisions affecting them and their privacy. Competition and data protection authorities within the EU cooperate and share their informations about their investigations. While data maximisation is clearly unsustainable from an environmental perspective, within the EU, data minimisation is a core principle of data protection law. Personal data should serve the public interest of state and society rather than private companies based mostly in the US and China.

In case of its proper enforcement, GDPR may be an effective tool of transparent data processing in the EU, and can serve as a model for the rest of the world. Enforcement is duty of the member states’ authorities. Therefore, Buttarelli’s views and Hungarian data protection’s legal tools are worth a comparative analysis.

" } ["copyrightHolder"]=> array(1) { ["en_US"]=> string(17) "Páll Imre Borisz" } ["subtitle"]=> array(1) { ["en_US"]=> string(88) "Observations on Buttarelli’s Privacy 2030 in the Context Of Data Protection in Hungary" } ["title"]=> array(2) { ["en_US"]=> string(23) "From Vision to Practice" ["hu_HU"]=> string(154) "A CRITICAL REVIEW OF THE THEORY OF DATA PROTECTION PROVIDED BY HUNGARIAN PUBLIC ADMINISTRATION IN COMPARISON WITH GIOVANNI BUTTARELLI'S „PRIVACY 2030”" } ["locale"]=> string(5) "en_US" ["authors"]=> array(1) { [0]=> object(Author)#806 (6) { ["_data"]=> array(14) { ["id"]=> int(8026) ["email"]=> string(18) "ibpall17@gmail.com" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(6511) ["seq"]=> int(1) ["userGroupId"]=> int(116) ["country"]=> string(0) "" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(5) "Páll" ["hu_HU"]=> string(5) "Páll" } ["givenName"]=> array(2) { ["en_US"]=> string(11) "Imre Borisz" ["hu_HU"]=> string(11) "Imre Borisz" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(2) { ["hu_HU"]=> array(1) { [0]=> string(98) "Data protection, GDPR, Public administration, Privacy, European Union, New technologies, Democracy" } ["en_US"]=> array(6) { [0]=> string(15) "data protection" [1]=> string(9) "democracy" [2]=> string(14) "European Union" [3]=> string(4) "GDPR" [4]=> string(16) "new technologies" [5]=> string(21) "public administration" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#819 (7) { ["_submissionFile"]=> NULL ["_data"]=> array(9) { ["submissionFileId"]=> int(34654) ["id"]=> int(5955) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(6511) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }
PDF