{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:09:59Z","timestamp":1743120599603,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030684488"},{"type":"electronic","value":"9783030684495"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68449-5_44","type":"book-chapter","created":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T15:50:40Z","timestamp":1612540240000},"page":"458-468","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Detecting Arson and Stone Pelting in Extreme Violence: A Deep Learning Based Identification Approach"],"prefix":"10.1007","author":[{"given":"Gaurav","family":"Tripathi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuldeep","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinesh Kumar","family":"Vishwakarma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,6]]},"reference":[{"key":"44_CR1","unstructured":"https:\/\/www.merriam-webster.com\/dictionary\/protest, https:\/\/www.merriam-webster.com\/dictionary\/protest. Accessed 6 July 2020"},{"key":"44_CR2","unstructured":"https:\/\/en.wikipedia.org\/wiki\/Stone_pelting_in_Kashmir (2020). https:\/\/en.wikipedia.org\/wiki\/Stone_pelting_in_Kashmir. Accessed 29 Sep 2020"},{"key":"44_CR3","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730\u20133738 (2015)","DOI":"10.1109\/ICCV.2015.425"},{"issue":"1","key":"44_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1140\/epjds\/s13688-015-0056-y","volume":"4","author":"ZC Steinert-Threlkeld","year":"2015","unstructured":"Steinert-Threlkeld, Z.C., Mocanu, D., Vespignani, A., Fowler, J.: Online social networks and offline protest. EPJ Data Sci. 4(1), 1\u20139 (2015). https:\/\/doi.org\/10.1140\/epjds\/s13688-015-0056-y","journal-title":"EPJ Data Sci."},{"key":"44_CR5","doi-asserted-by":"crossref","unstructured":"Leetaru, K., Wang, S., Cao, G., Padmanabhan, A., Shook, E.: Mapping the global Twitter heartbeat: the geography of Twitter. First Monday 18(5) (2013)","DOI":"10.5210\/fm.v18i5.4366"},{"key":"44_CR6","doi-asserted-by":"crossref","unstructured":"Redi, M., O'Hare, N., Schifanella, R., Trevisiol, M., Jaimes, A.: 6 seconds of sound and vision: creativity in micro-videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4272\u20134279 (2014)","DOI":"10.1109\/CVPR.2014.544"},{"key":"44_CR7","unstructured":"https:\/\/indianexpress.com\/article\/world\/israeli-troops-killed-two-stone-pelters-in-west-bank-palestinian-officials-4746966\/. Accessed 29 Sep 2020"},{"key":"44_CR8","unstructured":"Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"issue":"3","key":"44_CR9","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"44_CR10","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"44_CR11","unstructured":"https:\/\/en.wikipedia.org\/wiki\/Stone_pelting_in_Kashmir#:~:text=On%2025%20October%202018%2C%20an,22%20year%20old%20from%20Uttarakhand. Accessed 29 Sep 2020"},{"key":"44_CR12","unstructured":"Krizhevsky, A., Sutskever, I., Geoffrey, E.H.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS 2012), vol. 25 (2012)"},{"key":"44_CR13","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances Neural Information Processing Systems (2014)"},{"key":"44_CR14","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"44_CR15","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems (2010)","DOI":"10.1109\/ISCAS.2010.5537907"},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Perez, M., Kot, A.C., Rocha, A.: Detection of real-world fights in surveillance videos. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019)","DOI":"10.1109\/ICASSP.2019.8683676"},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"Torrey, L., Shavlik, J.: Transfer learning. In: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, pp. 242\u2013264. IGI global (2010)","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"key":"44_CR18","volume-title":"Transfer learning & the art of using pre-trained Models in deep learning","author":"D Gupta","year":"2017","unstructured":"Gupta, D., Jain, S., Shaikh, F., Singh, G.: Transfer learning & the art of using pre-trained Models in deep learning. Anal. Vidhya (2017)"},{"key":"44_CR19","doi-asserted-by":"crossref","unstructured":"Tufekci, Z.: Big questions for social media big data: representativeness, validity and other methodological pitfalls. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)","DOI":"10.1609\/icwsm.v8i1.14517"},{"issue":"1","key":"44_CR20","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1086\/683187","volume":"78","author":"AT Little","year":"2016","unstructured":"Little, A.T.: Communication technology and protest. J. Polit. 78(1), 152\u2013166 (2016)","journal-title":"J. Polit."},{"key":"44_CR21","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1111\/j.1533-8525.2000.tb00075.x","volume":"41","author":"G Yang","year":"2000","unstructured":"Yang, G.: Achieving emotions in collective action: emotional processes and movement mobilization in the 1989 Chinese student movement. Sociol. Q. 41, 593\u2013614 (2000)","journal-title":"Sociol. Q."},{"key":"44_CR22","doi-asserted-by":"crossref","unstructured":"Isola, P., Xiao, J., Torralba, A., Oliva, A.: What makes an image memorable? In: CVPR 2011, pp. 145\u2013152. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995721"},{"key":"44_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-319-04114-8_13","volume-title":"MultiMedia Modeling","author":"G Petkos","year":"2014","unstructured":"Petkos, G., Papadopoulos, S., Schinas, E., Kompatsiaris, Y.: Graph-based multimodal clustering for social event detection in large collections of images. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O\u2019Connor, N. (eds.) MMM 2014. LNCS, vol. 8325, pp. 146\u2013158. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-04114-8_13"},{"key":"44_CR24","unstructured":"Gonz\u00e1lez-Bail\u00f3n, S., Borge-Holthoefer, J., Moreno, Y.: Broadcasters and hidden influentials in online protest diffusion. Am. Behav. Sci. 57(7), 943\u2013965 (2013)"},{"key":"44_CR25","unstructured":"Fisher, D.R.: Studying Large-Scale Protest: Understanding Mobilization and Participation at the People\u2019s Climate, March (2014). http:\/\/www.sindark.com\/phd\/thesis\/sources\/PCM_PreliminaryResults.pdf"},{"key":"44_CR26","doi-asserted-by":"crossref","unstructured":"Parikh, D., Grauman, K.: Relative attributes. In: 2011 International Conference on Computer Vision, pp. 503\u2013510. IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126281"},{"key":"44_CR27","doi-asserted-by":"crossref","unstructured":"Petkos, G., Papadopoulos, S., Kompatsiaris, Y.: Social event detection using multimodal clustering and integrating supervisory signals. In: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, pp. 1\u20138 (2012)","DOI":"10.1145\/2324796.2324825"},{"key":"44_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/978-3-030-11012-3_24","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"A Hanson","year":"2019","unstructured":"Hanson, A., PNVR, K., Krishnagopal, S., Davis, L.: Bidirectional convolutional LSTM for the detection of violence in videos. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11130, pp. 280\u2013295. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11012-3_24"},{"key":"44_CR29","doi-asserted-by":"crossref","unstructured":"Sumon, S.A., Shahria, M.T., Goni, M.R., Hasan, N., Almarufuzzaman, A., Rahman, R.M.: Violent crowd flow detection using DEEP learning. In: Asian Conference on Intelligent Information and Database Systems (2019)","DOI":"10.1007\/978-3-030-14799-0_53"},{"key":"44_CR30","doi-asserted-by":"crossref","unstructured":"Mu, G., Cao, H., Jin, Q.: Violent scene detection using convolutional neural networks and deep audio feature. In: Chinese Conference on Pattern Recognition (2016)","DOI":"10.1007\/978-981-10-3005-5_37"},{"key":"44_CR31","doi-asserted-by":"crossref","unstructured":"Won, D., Steinert-Threlkeld, Z.C., Joo, J.: Protest activity detection and perceived violence estimation from social media images. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 786\u2013794 (2017)","DOI":"10.1145\/3123266.3123282"},{"key":"44_CR32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"44_CR33","unstructured":"Harvey, M.: Creating insanely fast image classifiers with MobileNet in TensorFlow. Hacker Noon (2017)"},{"key":"44_CR34","unstructured":"https:\/\/github.com\/ostrolucky\/Bulk-Bing-Image-downloader. Accessed 06 July 2020"},{"key":"44_CR35","unstructured":"https:\/\/neurohive.io\/en\/popular-networks\/vgg16\/, https:\/\/neurohive.io\/en\/popular-networks\/vgg16\/. Accessed 05 Sep 2020"},{"key":"44_CR36","unstructured":"https:\/\/alexisbcook.github.io\/2017\/using-transfer-learning-to-classify-images-with-keras\/. Accessed 05 Sep 2020"},{"key":"44_CR37","unstructured":"Chollet, F.: o. \"Keras,\" GitHub (2015)"}],"container-title":["Lecture Notes in Computer Science","Intelligent Human Computer Interaction"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68449-5_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:30:13Z","timestamp":1671064213000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68449-5_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030684488","9783030684495"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68449-5_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IHCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Human Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daegu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ihci2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"185","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}