Littérature scientifique sur le sujet « Abuse detection »
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Articles de revues sur le sujet "Abuse detection"
Kiyohara, Sheri M. « Child Abuse Detection ». Journal of Child Sexual Abuse 4, no 2 (septembre 1995) : 105–8. http://dx.doi.org/10.1300/j070v04n02_07.
Texte intégralA. Harries, Priscilla, Miranda L. Davies, Kenneth J. Gilhooly, Mary L.M. Gilhooly et Deborah Cairns. « Detection and prevention of financial abuse against elders ». Journal of Financial Crime 21, no 1 (20 décembre 2013) : 84–99. http://dx.doi.org/10.1108/jfc-05-2013-0040.
Texte intégralde la Parte-Serna, Alejandro Carlos, Gonzalo Oliván-Gonzalvo, Cosmina Raluca Fratila, Mariona Hermoso-Vallespí, Andrea Peiró-Aubalat et Ricardo Ortega-Soria. « The dark side of Paediatric dentistry : Child abuse ». Iberoamerican Journal of Medicine 2, no 3 (5 avril 2020) : 194–200. http://dx.doi.org/10.53986/ibjm.2020.0035.
Texte intégralMarlinda, Evy, Syamsul Firdaus et Haitami Haitami. « DILAN (DETEKSI DINI-LANJUT) NARKOBA PELAJAR SMPN-3 KECAMATAN CEMPAKA KOTA BANJARBARU ». Jurnal Rakat Sehat : Pengabdian Kepada Masyarakat 1, no 1 (22 avril 2022) : 14–19. http://dx.doi.org/10.31964/jrs.v1i1.5.
Texte intégralCoyne, John F., David King, Steven Garin et Allen Fred Fielding. « Detection of child abuse ». British Journal of Oral and Maxillofacial Surgery 35, no 6 (décembre 1997) : 448. http://dx.doi.org/10.1016/s0266-4356(97)90755-5.
Texte intégralRohringer, Taryn J., Tony E. Rosen, Mihan R. Lee, Pallavi Sagar et Kieran J. Murphy. « Can diagnostic imaging help improve elder abuse detection ? » British Journal of Radiology 93, no 1110 (juin 2020) : 20190632. http://dx.doi.org/10.1259/bjr.20190632.
Texte intégralBahrami, Pouneh Nikkhah, Umar Iqbal et Zubair Shafiq. « FP-Radar : Longitudinal Measurement and Early Detection of Browser Fingerprinting ». Proceedings on Privacy Enhancing Technologies 2022, no 2 (3 mars 2022) : 557–77. http://dx.doi.org/10.2478/popets-2022-0056.
Texte intégralXu, Shujuan, Biao Ma, Jiali Li, Wei Su, Tianran Xu et Mingzhou Zhang. « Europium Nanoparticles-Based Fluorescence Immunochromatographic Detection of Three Abused Drugs in Hair ». Toxics 11, no 5 (29 avril 2023) : 417. http://dx.doi.org/10.3390/toxics11050417.
Texte intégralBrown, Sarah D., Greg Brack et Frances Y. Mullis. « Traumatic Symptoms in Sexually Abused Children : Implications for School Counselors ». Professional School Counseling 11, no 6 (août 2008) : 2156759X0801100. http://dx.doi.org/10.1177/2156759x0801100603.
Texte intégralS, Srividya M., Anala M. R et Chetan Tayal. « Deep learning techniques for physical abuse detection ». IAES International Journal of Artificial Intelligence (IJ-AI) 10, no 4 (1 décembre 2021) : 971. http://dx.doi.org/10.11591/ijai.v10.i4.pp971-981.
Texte intégralThèses sur le sujet "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.
Texte intégralLamping, 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.
Texte intégralFaulds, 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.
Texte intégralBadiru, 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.
Texte intégralMansell, 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.
Texte intégralWang, Ling. « Applications of Paper Microfluidic Systems in the Field Detection of Drugs of Abuse ». FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3381.
Texte intégralCécillon, 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.
Texte intégralOnline 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.
Trouver le texte intégralMwenesongole, 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/.
Texte intégralLow, 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.
Texte intégralLivres sur le sujet "Abuse detection"
Mwiti, Gladys. Child abuse : Detection, prevention, and counselling. Nairobi, Kenya : Evangel Pub. House, 2006.
Trouver le texte intégralMedical Express. Professional Development Center., dir. The prevention and detection of elder abuse. San Diego, CA (12235 El Camino Real, Suite 200, San Diego 92130) : MedicalExpress Professional Development Center, 2000.
Trouver le texte intégralMedical Express. Professional Development Center., dir. The prevention and detection of elder abuse. San Diego, CA (12235 El Camino Real, Suite 200, San Diego 92130) : MedicalExpress Professional Development Center, 2000.
Trouver le texte intégralMedical Express. Professional Development Center., dir. The prevention and detection of elder abuse. San Diego, CA (12235 El Camino Real, Suite 200, San Diego 92130) : MedicalExpress Professional Development Center, 2000.
Trouver le texte intégralBonnie, Brandl, dir. Elder abuse detection and intervention : A collaborative approach. New York : Springer, 2007.
Trouver le texte intégralOccult crime : Detection, investigation, and verification. Las Vegas, N.M : San Miguel Press, 1992.
Trouver le texte intégralMiller, Gary J. Drugs and the law : Detection, recognition & investigation. [Altamonte Springs, FL] : Gould Publications, 1992.
Trouver le texte intégralMiller, Gary J. Drugs and the law : Detection, recognition & investigation. Charlottesville, VA : LexisNexis, 2014.
Trouver le texte intégralReno 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.
Trouver le texte intégralA, Burtonwood C., et Great Britain. Medicines and Healthcare products Regulatory Agency., dir. Sixteen devices for the detection of drugs of abuse in urine. London : MRHA, 2003.
Trouver le texte intégralChapitres de livres sur le sujet "Abuse detection"
Kumar, Ayush, Aryan Nigam, Aradhana Tripathi, Aftab Khan, Nigam Kumar Mishra et Rochak Bajpai. « Real-time-abuse detection model ». Dans Advances in AI for Biomedical Instrumentation, Electronics and Computing, 595–99. London : CRC Press, 2024. http://dx.doi.org/10.1201/9781032644752-108.
Texte intégralKong, Chao, Jianye Liu, Hao Li, Ying Liu, Haibei Zhu et Tao Liu. « Drug Abuse Detection via Broad Learning ». Dans Web Information Systems and Applications, 499–505. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30952-7_49.
Texte intégralChen, Yizheng, Panagiotis Kintis, Manos Antonakakis, Yacin Nadji, David Dagon, Wenke Lee et Michael Farrell. « Financial Lower Bounds of Online Advertising Abuse ». Dans 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.
Texte intégralRao, Udai Pratap, et Nikhil Kumar Singh. « Detection of Privilege Abuse in RBAC Administered Database ». Dans Studies in Computational Intelligence, 57–76. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14654-6_4.
Texte intégralSchänzer, Wilhelm. « Abuse of androgens and detection of illegal use ». Dans Testosterone, 545–65. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72185-4_20.
Texte intégralPapegnies, Etienne, Vincent Labatut, Richard Dufour et Georges Linarès. « Graph-Based Features for Automatic Online Abuse Detection ». Dans Statistical Language and Speech Processing, 70–81. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68456-7_6.
Texte intégralNelson, Anne E., et Ken K. Y. Ho. « Detection of Growth Hormone Doping in Sport Using Growth Hormone-Responsive Markers ». Dans 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.
Texte intégralBelkowski, Stanley M., Jinmin Zhu, Lee Y. Liu-Chen, Toby K. Eisenstein, Martin W. Adler et Thomas J. Rogers. « Detection of К-Opioid Receptor mRNA in Immature T Cells ». Dans 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.
Texte intégralPapegnies, Etienne, Vincent Labatut, Richard Dufour et Georges Linarès. « Impact of Content Features for Automatic Online Abuse Detection ». Dans 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.
Texte intégralGarcía-Recuero, Álvaro, Jeffrey Burdges et Christian Grothoff. « Privacy-Preserving Abuse Detection in Future Decentralised Online Social Networks ». Dans 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.
Texte intégralActes de conférences sur le sujet "Abuse detection"
Sharon, Rini, Heet Shah, Debdoot Mukherjee et Vikram Gupta. « Multilingual and Multimodal Abuse Detection ». Dans Interspeech 2022. ISCA : ISCA, 2022. http://dx.doi.org/10.21437/interspeech.2022-10629.
Texte intégralWang, Andrew Z., Rex Ying, Pan Li, Nikhil Rao, Karthik Subbian et Jure Leskovec. « Bipartite Dynamic Representations for Abuse Detection ». Dans 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.
Texte intégralFa, Zhou, Guang-Gang Geng, Zhi-Wei Yan et Xiao-Dong Lee. « A robust internet abuse detection method ». Dans 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258113.
Texte intégralWhiton, Adam, et Yolita Nugent. « A Wearable for Physical Abuse Detection ». Dans 2007 11th IEEE International Symposium on Wearable Computers. IEEE, 2007. http://dx.doi.org/10.1109/iswc.2007.4373796.
Texte intégralGupta, Vikram, Rini Sharon, Ramit Sawhney et Debdoot Mukherjee. « ADIMA : Abuse Detection In Multilingual Audio ». Dans ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9746718.
Texte intégralMedvedeva, Marina. « AUTOMATIC DETECTION OF ABUSE ON SOCIAL MEDIA ». Dans 16th International Multidisciplinary Scientific GeoConference SGEM2016. Stef92 Technology, 2016. http://dx.doi.org/10.5593/sgem2016/b21/s07.013.
Texte intégralThakran, Yash, et Vinayak Abrol. « Investigating Acoustic Cues for Multilingual Abuse Detection ». Dans INTERSPEECH 2023. ISCA : ISCA, 2023. http://dx.doi.org/10.21437/interspeech.2023-1311.
Texte intégralPalanikumar, Vasanth, Sean Benhur, Adeep Hande et Bharathi Raja Chakravarthi. « DE-ABUSE@TamilNLP-ACL 2022 : Transliteration as Data Augmentation for Abuse Detection in Tamil ». Dans 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.
Texte intégralMalte, Aditya, et Pratik Ratadiya. « Multilingual Cyber Abuse Detection using Advanced Transformer Architecture ». Dans TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). IEEE, 2019. http://dx.doi.org/10.1109/tencon.2019.8929493.
Texte intégralFounta, Antigoni Maria, Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Athena Vakali et Ilias Leontiadis. « A Unified Deep Learning Architecture for Abuse Detection ». Dans the 10th ACM Conference. New York, New York, USA : ACM Press, 2019. http://dx.doi.org/10.1145/3292522.3326028.
Texte intégralRapports d'organisations sur le sujet "Abuse detection"
Peter W. Carr, K.M. Fuller, D.R. Stoll, L.D. Steinkraus, M.S. Pasha et 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), décembre 2005. http://dx.doi.org/10.2172/892807.
Texte intégralBecker, David, Daniel Kessler et Mark McClellan. Detecting Medicare Abuse. Cambridge, MA : National Bureau of Economic Research, août 2004. http://dx.doi.org/10.3386/w10677.
Texte intégralBotulinum Neurotoxin-Producing Clostridia, Working Group on. Report on Botulinum Neurotoxin-Producing Clostridia. Food Standards Agency, août 2023. http://dx.doi.org/10.46756/sci.fsa.ozk974.
Texte intégralSteinman, Dave, Mike Celiceo et 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, juin 1999. http://dx.doi.org/10.21236/ada385478.
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