Добірка наукової літератури з теми "Abuse detection"
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Статті в журналах з теми "Abuse detection":
Kiyohara, Sheri M. "Child Abuse Detection." Journal of Child Sexual Abuse 4, no. 2 (September 1995): 105–8. http://dx.doi.org/10.1300/j070v04n02_07.
A. Harries, Priscilla, Miranda L. Davies, Kenneth J. Gilhooly, Mary L.M. Gilhooly, and Deborah Cairns. "Detection and prevention of financial abuse against elders." Journal of Financial Crime 21, no. 1 (December 20, 2013): 84–99. http://dx.doi.org/10.1108/jfc-05-2013-0040.
de la Parte-Serna, Alejandro Carlos, Gonzalo Oliván-Gonzalvo, Cosmina Raluca Fratila, Mariona Hermoso-Vallespí, Andrea Peiró-Aubalat, and Ricardo Ortega-Soria. "The dark side of Paediatric dentistry: Child abuse." Iberoamerican Journal of Medicine 2, no. 3 (April 5, 2020): 194–200. http://dx.doi.org/10.53986/ibjm.2020.0035.
Marlinda, Evy, Syamsul Firdaus, and Haitami Haitami. "DILAN (DETEKSI DINI-LANJUT) NARKOBA PELAJAR SMPN-3 KECAMATAN CEMPAKA KOTA BANJARBARU." Jurnal Rakat Sehat : Pengabdian Kepada Masyarakat 1, no. 1 (April 22, 2022): 14–19. http://dx.doi.org/10.31964/jrs.v1i1.5.
Coyne, John F., David King, Steven Garin, and Allen Fred Fielding. "Detection of child abuse." British Journal of Oral and Maxillofacial Surgery 35, no. 6 (December 1997): 448. http://dx.doi.org/10.1016/s0266-4356(97)90755-5.
Rohringer, Taryn J., Tony E. Rosen, Mihan R. Lee, Pallavi Sagar, and Kieran J. Murphy. "Can diagnostic imaging help improve elder abuse detection?" British Journal of Radiology 93, no. 1110 (June 2020): 20190632. http://dx.doi.org/10.1259/bjr.20190632.
Bahrami, Pouneh Nikkhah, Umar Iqbal, and Zubair Shafiq. "FP-Radar: Longitudinal Measurement and Early Detection of Browser Fingerprinting." Proceedings on Privacy Enhancing Technologies 2022, no. 2 (March 3, 2022): 557–77. http://dx.doi.org/10.2478/popets-2022-0056.
Xu, Shujuan, Biao Ma, Jiali Li, Wei Su, Tianran Xu, and Mingzhou Zhang. "Europium Nanoparticles-Based Fluorescence Immunochromatographic Detection of Three Abused Drugs in Hair." Toxics 11, no. 5 (April 29, 2023): 417. http://dx.doi.org/10.3390/toxics11050417.
Brown, Sarah D., Greg Brack, and Frances Y. Mullis. "Traumatic Symptoms in Sexually Abused Children: Implications for School Counselors." Professional School Counseling 11, no. 6 (August 2008): 2156759X0801100. http://dx.doi.org/10.1177/2156759x0801100603.
S, Srividya M., Anala M. R, and Chetan Tayal. "Deep learning techniques for physical abuse detection." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (December 1, 2021): 971. http://dx.doi.org/10.11591/ijai.v10.i4.pp971-981.
Дисертації з теми "Abuse detection":
Abbott, R. W. "HPLC of drugs of abuse with chemiluminescence detection." Thesis, University of Hull, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384671.
Lamping, Sarah Louise. "Study of SERS for the detection of drugs of abuse." Thesis, University of Strathclyde, 2008. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21989.
Faulds, Karen Jade. "Detection of drugs of abuse by surface enhanced Raman scattering (SERS)." Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288636.
Badiru, Shewu Oladapo. "Chromatographic studies on the detection of some basic drugs of abuse." Thesis, University of Bath, 1989. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234086.
Mansell, Sheila L. "Sexual abuse detection, sequelae, and therapy accommodations for people with developmental disabilities." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq23027.pdf.
Wang, Ling. "Applications of Paper Microfluidic Systems in the Field Detection of Drugs of Abuse." FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3381.
Cécillon, Noé. "Combining Graph and Text to Model Conversations : An Application to Online Abuse Detection." Electronic Thesis or Diss., Avignon, 2024. http://www.theses.fr/2024AVIG0100.
Online abusive behaviors can have devastating consequences on individuals and communities. With the global expansion of internet and the social networks, anyone can be confronted with these behaviors. Over the past few years, laws and regulations have been established to regulate this kind of abuse but the responsibility ultimately lies with the platforms that host online communications. They are asked to monitor their users in order to prevent the proliferation of abusive content. Timely detection and moderation is a key factor to reduce the quantity and impact of abusive behaviors. However, due to the sheer quantity of online messages posted every day, platforms struggle to provide adequate resources. Since this implies high human and financial costs, companies have a keen interest in automating this process. Although it may seem a relatively simple task, it turns out to be quite complex. Indeed, malicious users have developed numerous techniques to bypass the standard automated methods. Allusions or implied meaning are other examples of strategies that automatic methods struggle to detect. While usually performed on individual messages taken out of their context, it has been shown that automatic abuse detection can benefit from considering the context in which the message was posted. In this thesis, we want to focus on the combination of content and structure of conversations to tackle the abuse detection task. Using the textual content of messages is the standard approach which was first developed in the literature. It has the advantage of being easy to set up, but on the other hand, it is vulnerable to text-based attacks such as obfuscation. The structure of the conversation which represent the context is less frequently used as it is more complicated to manipulate. Yet it allows to introduce a contextual aspect which helps detecting abuse occurrences when the text on its own is not sufficient. This context can be modeled as a contextual graph representing the conversation which includes the message. By comparing two methods based on feature engineering on a dataset of conversations extracted from a video games, we could show that a method relying exclusively on conversational graphs and ignoring the content was able to obtain better detection performance. The literature suggest that combining multiple modalities often result in a better detection of abusive messages. We propose multiple strategies to combine the content and structure of conversations and prove that their combination is indeed beneficial to the detection. A limitation of feature-based methods is that they are costly in time and computational resources. Our study also highlights that only a fraction of the computed features are truly relevant for the task. Representation learning methods can be used to mitigate these issues by automatically learning the representations of text and conversational graphs. For graphs, we demonstrated that using edge weights, signs and directions improved the performance. As no method exists for signed whole-graph embedding, we fill this gap in the literature by developing two such methods. We assess them on a newly constituted benchmark of three datasets of signed graphs and show that they perform better than their unsigned counterparts. Lastly, we perform a comparative study of several lexical and graph-embedding method for abuse detection by applying them to our dataset of conversations. Our results show that they perform better than feature-based approaches on text and are slightly less effective on graphs. Still, they obtain promising results given that they are completely task independent, much more scalable and time-efficient than feature-based approaches
Yang, Li. "A comparison of unsupervised learning techniques for detection of medical abuse in automobile claims." California State University, Long Beach, 2013.
Mwenesongole, Ellen Musili. "Simultaneous detection of drugs of abuse in waste water using gas chromatography-mass spectrometry." Thesis, Anglia Ruskin University, 2015. http://arro.anglia.ac.uk/550378/.
Low, Ann Stewart. "An evaluation of analytical procedures for detection of drug abuse with particular reference to opiates." Thesis, Robert Gordon University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242985.
Книги з теми "Abuse detection":
Mwiti, Gladys. Child abuse: Detection, prevention, and counselling. Nairobi, Kenya: Evangel Pub. House, 2006.
Medical Express. Professional Development Center., ed. The prevention and detection of elder abuse. San Diego, CA (12235 El Camino Real, Suite 200, San Diego 92130): MedicalExpress Professional Development Center, 2000.
Medical Express. Professional Development Center., ed. The prevention and detection of elder abuse. San Diego, CA (12235 El Camino Real, Suite 200, San Diego 92130): MedicalExpress Professional Development Center, 2000.
Medical Express. Professional Development Center., ed. The prevention and detection of elder abuse. San Diego, CA (12235 El Camino Real, Suite 200, San Diego 92130): MedicalExpress Professional Development Center, 2000.
Bonnie, Brandl, ed. Elder abuse detection and intervention: A collaborative approach. New York: Springer, 2007.
Dubois, William Edward Lee. Occult crime: Detection, investigation, and verification. Las Vegas, N.M: San Miguel Press, 1992.
Miller, Gary J. Drugs and the law: Detection, recognition & investigation. [Altamonte Springs, FL]: Gould Publications, 1992.
Miller, Gary J. Drugs and the law: Detection, recognition & investigation. Charlottesville, VA: LexisNexis, 2014.
Reno Conference on the Integration of Behavioral Health in Primary Care: Beyond Efficacy to Effectiveness. Early detection and treatment of substance abuse within integrated primary care. Reno, NV: Context Press, 2006.
A, Burtonwood C., and Great Britain. Medicines and Healthcare products Regulatory Agency., eds. Sixteen devices for the detection of drugs of abuse in urine. London: MRHA, 2003.
Частини книг з теми "Abuse detection":
Kumar, Ayush, Aryan Nigam, Aradhana Tripathi, Aftab Khan, Nigam Kumar Mishra, and Rochak Bajpai. "Real-time-abuse detection model." In Advances in AI for Biomedical Instrumentation, Electronics and Computing, 595–99. London: CRC Press, 2024. http://dx.doi.org/10.1201/9781032644752-108.
Kong, Chao, Jianye Liu, Hao Li, Ying Liu, Haibei Zhu, and Tao Liu. "Drug Abuse Detection via Broad Learning." In Web Information Systems and Applications, 499–505. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30952-7_49.
Chen, Yizheng, Panagiotis Kintis, Manos Antonakakis, Yacin Nadji, David Dagon, Wenke Lee, and Michael Farrell. "Financial Lower Bounds of Online Advertising Abuse." In Detection of Intrusions and Malware, and Vulnerability Assessment, 231–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40667-1_12.
Rao, Udai Pratap, and Nikhil Kumar Singh. "Detection of Privilege Abuse in RBAC Administered Database." In Studies in Computational Intelligence, 57–76. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14654-6_4.
Schänzer, Wilhelm. "Abuse of androgens and detection of illegal use." In Testosterone, 545–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72185-4_20.
Papegnies, Etienne, Vincent Labatut, Richard Dufour, and Georges Linarès. "Graph-Based Features for Automatic Online Abuse Detection." In Statistical Language and Speech Processing, 70–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68456-7_6.
Nelson, Anne E., and Ken K. Y. Ho. "Detection of Growth Hormone Doping in Sport Using Growth Hormone-Responsive Markers." In Hormone Use and Abuse by Athletes, 139–50. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7014-5_15.
Belkowski, Stanley M., Jinmin Zhu, Lee Y. Liu-Chen, Toby K. Eisenstein, Martin W. Adler, and Thomas J. Rogers. "Detection of К-Opioid Receptor mRNA in Immature T Cells." In The Brain Immune Axis and Substance Abuse, 11–16. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1951-5_2.
Papegnies, Etienne, Vincent Labatut, Richard Dufour, and Georges Linarès. "Impact of Content Features for Automatic Online Abuse Detection." In Computational Linguistics and Intelligent Text Processing, 404–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77116-8_30.
García-Recuero, Álvaro, Jeffrey Burdges, and Christian Grothoff. "Privacy-Preserving Abuse Detection in Future Decentralised Online Social Networks." In Data Privacy Management and Security Assurance, 78–93. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47072-6_6.
Тези доповідей конференцій з теми "Abuse detection":
Sharon, Rini, Heet Shah, Debdoot Mukherjee, and Vikram Gupta. "Multilingual and Multimodal Abuse Detection." In Interspeech 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/interspeech.2022-10629.
Wang, Andrew Z., Rex Ying, Pan Li, Nikhil Rao, Karthik Subbian, and Jure Leskovec. "Bipartite Dynamic Representations for Abuse Detection." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467141.
Fa, Zhou, Guang-Gang Geng, Zhi-Wei Yan, and Xiao-Dong Lee. "A robust internet abuse detection method." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258113.
Whiton, Adam, and Yolita Nugent. "A Wearable for Physical Abuse Detection." In 2007 11th IEEE International Symposium on Wearable Computers. IEEE, 2007. http://dx.doi.org/10.1109/iswc.2007.4373796.
Gupta, Vikram, Rini Sharon, Ramit Sawhney, and Debdoot Mukherjee. "ADIMA: Abuse Detection In Multilingual Audio." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9746718.
Medvedeva, Marina. "AUTOMATIC DETECTION OF ABUSE ON SOCIAL MEDIA." In 16th International Multidisciplinary Scientific GeoConference SGEM2016. Stef92 Technology, 2016. http://dx.doi.org/10.5593/sgem2016/b21/s07.013.
Thakran, Yash, and Vinayak Abrol. "Investigating Acoustic Cues for Multilingual Abuse Detection." In INTERSPEECH 2023. ISCA: ISCA, 2023. http://dx.doi.org/10.21437/interspeech.2023-1311.
Palanikumar, Vasanth, Sean Benhur, Adeep Hande, and Bharathi Raja Chakravarthi. "DE-ABUSE@TamilNLP-ACL 2022: Transliteration as Data Augmentation for Abuse Detection in Tamil." In Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.dravidianlangtech-1.5.
Malte, Aditya, and Pratik Ratadiya. "Multilingual Cyber Abuse Detection using Advanced Transformer Architecture." In TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). IEEE, 2019. http://dx.doi.org/10.1109/tencon.2019.8929493.
Founta, Antigoni Maria, Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Athena Vakali, and Ilias Leontiadis. "A Unified Deep Learning Architecture for Abuse Detection." In the 10th ACM Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3292522.3326028.
Звіти організацій з теми "Abuse detection":
Peter W. Carr, K.M. Fuller, D.R. Stoll, L.D. Steinkraus, M.S. Pasha, and Glenn G. Hardin. Fast Gradient Elution Reversed-Phase HPLC with Diode-Array Detection as a High Throughput Screening Method for Drugs of Abuse. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/892807.
Becker, David, Daniel Kessler, and Mark McClellan. Detecting Medicare Abuse. Cambridge, MA: National Bureau of Economic Research, August 2004. http://dx.doi.org/10.3386/w10677.
Botulinum Neurotoxin-Producing Clostridia, Working Group on. Report on Botulinum Neurotoxin-Producing Clostridia. Food Standards Agency, August 2023. http://dx.doi.org/10.46756/sci.fsa.ozk974.
Steinman, Dave, Mike Celiceo, and Joe Head. Stopping Insider Abuse and Spying. Detecting the Hard Stuff: Stolen Passwords Unauthorized Records Browsing, Employee Espionage, Infiltration, and Insertion of Unwelcome Code, via Automatic Behavior Profiling. Fort Belvoir, VA: Defense Technical Information Center, June 1999. http://dx.doi.org/10.21236/ada385478.