Добірка наукової літератури з теми "In-app user activity detection"
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Статті в журналах з теми "In-app user activity detection"
Pathmaperuma, Madushi H., Yogachandran Rahulamathavan, Safak Dogan, and Ahmet Kondoz. "CNN for User Activity Detection Using Encrypted In-App Mobile Data." Future Internet 14, no. 2 (February 21, 2022): 67. http://dx.doi.org/10.3390/fi14020067.
Повний текст джерелаPathmaperuma, Madushi H., Yogachandran Rahulamathavan, Safak Dogan, and Ahmet M. Kondoz. "Deep Learning for Encrypted Traffic Classification and Unknown Data Detection." Sensors 22, no. 19 (October 9, 2022): 7643. http://dx.doi.org/10.3390/s22197643.
Повний текст джерелаZhu, Hao, and Georgios B. Giannakis. "Exploiting Sparse User Activity in Multiuser Detection." IEEE Transactions on Communications 59, no. 2 (February 2011): 454–65. http://dx.doi.org/10.1109/tcomm.2011.121410.090570.
Повний текст джерелаMitra, U., and H. V. Poor. "Activity detection in a multi-user environment." Wireless Personal Communications 3, no. 1-2 (1996): 149–74. http://dx.doi.org/10.1007/bf00333928.
Повний текст джерелаKim, Youngho, Tae Oh, and Jeongnyeo Kim. "Analyzing User Awareness of Privacy Data Leak in Mobile Applications." Mobile Information Systems 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/369489.
Повний текст джерелаParwez, Md Salik, Danda B. Rawat, and Moses Garuba. "Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network." IEEE Transactions on Industrial Informatics 13, no. 4 (August 2017): 2058–65. http://dx.doi.org/10.1109/tii.2017.2650206.
Повний текст джерелаBashir, Sulaimon Adebayo, Andrei Petrovski, and Daniel Doolan. "A framework for unsupervised change detection in activity recognition." International Journal of Pervasive Computing and Communications 13, no. 2 (June 5, 2017): 157–75. http://dx.doi.org/10.1108/ijpcc-03-2017-0027.
Повний текст джерелаLee, Jemin, and Hyungshin Kim. "QDroid: Mobile Application Quality Analyzer for App Market Curators." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/1740129.
Повний текст джерелаZain ul Abideen, Muhammad, Shahzad Saleem, and Madiha Ejaz. "VPN Traffic Detection in SSL-Protected Channel." Security and Communication Networks 2019 (October 29, 2019): 1–17. http://dx.doi.org/10.1155/2019/7924690.
Повний текст джерелаKarthikeyan, Dakshinamoorthy, Arun Sivakumar, and Chamundeswari Arumugam. "Android X-Ray - A system for Malware Detection in Android apps using Dynamic Analysis." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 19 (November 7, 2022): 264–71. http://dx.doi.org/10.37394/23209.2022.19.27.
Повний текст джерелаДисертації з теми "In-app user activity detection"
Myles, Kimberly. "Activity-Based Target Acquisition Methods for Use in Urban Environments." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28422.
Повний текст джерелаPh. D.
Henriksson, Mikael. "Implementation of a Hardware Coordinate Wise Descend Algorithm with Maximum Likelihood Estimator for Use in mMTC Activity Detection." Thesis, Linköpings universitet, Datorteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171071.
Повний текст джерелаHuang, Nai-Hsuan, and 黃乃軒. "User Activity Detection and Pilot Sequence Design for Uplink Grant-free NOMA in 5G Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/u9j7tk.
Повний текст джерелаWeaver, Christopher Jordan. "Development of PYRAMDS (Python for Radioisotope Analysis and Multi-Detector Suppression) code used in fission product detection limit improvements with the DGF Pixie-4 digital spectrometer." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-05-2711.
Повний текст джерелаtext
Jallad, Mahmoud 1979. "Performance of several diagnostic systems on detection of occlusal primary caries in permanent teeth." Thesis, 2014. http://hdl.handle.net/1805/6498.
Повний текст джерелаDetection of caries at an early stage is unequivocally essential for early preventive intervention. Longitudinal assessment of caries lesions, especially under the opaque preventive sealant, would be of utmost importance to the dental community. OBJECTIVES: The aim of this two-part in-vitro study is to evaluate the performance of multiple detection methods: The International Caries Detection and Assessment System (ICDAS); two quantitative light-induced fluorescence systems QLF; Inspektor™ Pro and QLF-D Biluminator™2 (Inspektor Research Systems B.V.; Amsterdam, The Netherlands); and photothermal radiometry and modulated luminescence (PTR/LUM) of The Canary System® (Quantum Dental Technologies; Toronto, Canada). All these are to be evaluated on their detection of caries on posterior human permanent teeth for 1) of primary occlusal lesions, and 2) under the sealant of primary occlusal lesions. METHODS: One hundred and twenty (N = 120) human posterior permanent teeth, selected in compliance with IU-IRB “Institutional Review Board” standards, with non-cavitated occlusal lesions ICDAS (scores 0 to 4) were divided into two equal groups. The second group (N = 60) received an opaque resin dental sealant (Delton® Light-Curing Pit and Fissure Sealant Opaque, Dentsply, York, PA). All lesions were assessed with each detection method twice in a random order except for ICDAS, which was not used following the placement of the sealant. Histological validation was used to compare methods in regard to sensitivity, specificity, % correct, and the area under receiver- operating characteristic curve (AUC). Intra-examiner repeatability and inter-examiner agreement were measured using intraclass correlation coefficient (ICC). RESULTS: 1) Of primary occlusal lesions, sensitivity, specificity, and AUC values were respectively: 0.82, 0.86 and 0.87 (ICDAS); 0.89, 0.60 and 0.90 (Inspektor Pro); 0.96, 0.57 and 0.94 (QLF-D Biluminator 2); and 0.85, 0.43 and 0.79 (The Canary System). Intra-examiner repeatability and inter-examiner agreement were respectively: 0.81 to 0.87: 0.72 (ICDAS); 0.49 to 0.97: 0.73 (Inspektor Pro); 0.96 to 0.99: 0.96 (QLF-D Biluminator 2); and 0.33 to 0.63: 0.48 (The Canary System). 2) Of primary occlusal lesions under the opaque dental sealants, sensitivity, specificity, and AUC values were respectively: 0.99, 0.03 and 0.67 (Inspektor Pro); 1.00, 0.00 and 0.70 (QLF-D Biluminator 2); and 0.54, 0.50 and 0.58 (The Canary System). Intra-examiner repeatability and inter-examiner agreement were respectively: 0.24 to 0.37: 0.29 (Inspektor Pro); 0.80 to 0.84: 0.74 (QLF-D Biluminator 2); and 0.22 to 0.47: 0.01 (The Canary System). CONCLUSION: Limited to these in-vitro conditions, 1) ICDAS remains the method of choice for detection of early caries lesion due to its adequately high accuracy and repeatability. QLF systems demonstrate potential in longitudinal monitoring due to an almost perfect repeatability of QLF-D Biluminator 2. The Canary System performance and repeatability were not acceptable as a valid method of early caries detection. 2) None of the methods demonstrated acceptable ability in detecting of occlusal caries under the opaque sealant. However, QLF-D Biluminator 2, with limitation to these in-vitro conditions and Delton opaque sealant, demonstrated a fair accuracy AUC (0.70) in detecting of caries under sealants at an experimental threshold of 12.5% ΔF.
Книги з теми "In-app user activity detection"
Meijer, Ewout H., and Bruno Verschuere. Detection Deception Using Psychophysiological and Neural Measures. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190612016.003.0010.
Повний текст джерелаShaikh, Mohd Faraz. Machine Learning in Detecting Auditory Sequences in Magnetoencephalography Data : Research Project in Computational Modelling and Simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.25368/2022.411.
Повний текст джерелаOurada, Jason D., and Kenneth L. Appelbaum. Intoxication and drugs in facilities. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199360574.003.0024.
Повний текст джерелаWalczak, Jean-Sébastien. Understanding the responsiveness of C-fibres. Edited by Paul Farquhar-Smith, Pierre Beaulieu, and Sian Jagger. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198834359.003.0006.
Повний текст джерелаUfimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.
Повний текст джерелаChinoy, Hector, and Robert G. Cooper. Polymyositis and dermatomyositis. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0124.
Повний текст джерелаHarper, Lorraine, and David Jayne. The patient with vasculitis. Edited by Giuseppe Remuzzi. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199592548.003.0160.
Повний текст джерелаЧастини книг з теми "In-app user activity detection"
Baek, Jonghun, Geehyuk Lee, Wonbae Park, and Byoung-Ju Yun. "Accelerometer Signal Processing for User Activity Detection." In Lecture Notes in Computer Science, 610–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30134-9_82.
Повний текст джерелаScardino, Giuseppe, Ignazio Infantino, and Filippo Vella. "Recognition of Human Identity by Detection of User Activity." In Lecture Notes in Computer Science, 49–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39345-7_6.
Повний текст джерелаTokhtabayev, Arnur, Anton Kopeikin, Nurlan Tashatov, and Dina Satybaldina. "Malware Analysis and Detection via Activity Trees in User-Dependent Environment." In Lecture Notes in Computer Science, 211–22. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65127-9_17.
Повний текст джерелаMärker, Marcus, Sebastian Wolf, Oliver Scharf, Daniel Plorin, and Tobias Teich. "KNX-Based Sensor Monitoring for User Activity Detection in AAL-environments." In Ambient Assisted Living and Daily Activities, 18–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13105-4_4.
Повний текст джерелаLukashin, Aleksey, Mikhail Popov, Dmitrii Timofeev, and Igor Mikhalev. "Employee Performance Analytics Approach Based on Anomaly Detection in User Activity." In Proceedings of International Scientific Conference on Telecommunications, Computing and Control, 321–31. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6632-9_28.
Повний текст джерелаAhmed, Faisal, and Marina Gavrilova. "Biometric-Based User Authentication and Activity Level Detection in a Collaborative Environment." In Transparency in Social Media, 165–80. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18552-1_9.
Повний текст джерелаOrtega, Jose Luis Gomez, Liangxiu Han, and Nicholas Bowring. "Modelling and Detection of User Activity Patterns for Energy Saving in Buildings." In Emerging Trends and Advanced Technologies for Computational Intelligence, 165–85. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33353-3_9.
Повний текст джерелаFlores-Martin, Daniel, Sergio Laso, Javier Berrocal, and Juan M. Murillo. "Contigo: Monitoring People’s Activity App for Anomalies Detection." In Lecture Notes in Bioengineering, 3–14. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97524-1_1.
Повний текст джерелаZeng, Fuwei, Tie Bao, and Wenhao Xiang. "Machine Learning in Short Video APP User Activity Prediction." In Human Centered Computing, 568–75. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37429-7_58.
Повний текст джерелаKleanthous, Styliani, Constantinos Herodotou, George Samaras, and Panayiotis Germanakos. "Detecting Personality Traces in Users’ Social Activity." In Social Computing and Social Media, 287–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39910-2_27.
Повний текст джерелаТези доповідей конференцій з теми "In-app user activity detection"
Hasara Pathmaperuma, Madushi, Yogachandran Rahulamathavan, Safak Dogan, and Ahmet M. Kondoz. "User Mobile App Encrypted Activity Detection." In ESCC '21: The 2nd European Symposium on Computer and Communications. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3478301.3478303.
Повний текст джерелаSwarnalaxmi, S., I. Elakkiya, M. Thilagavathi, Anil Thomas, and Gunasekaran Raja. "User Activity Analysis Driven Anomaly Detection in Cellular Network." In 2018 Tenth International Conference on Advanced Computing (ICoAC). IEEE, 2018. http://dx.doi.org/10.1109/icoac44903.2018.8939064.
Повний текст джерелаHu, Qiaona, Baoming Tang, and Derek Lin. "Anomalous User Activity Detection in Enterprise Multi-source Logs." In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.110.
Повний текст джерелаMarchenkov, Sergey, and Dmitry Korzun. "User presence detection based on tracking network activity in smartroom." In 2014 16th Conference of Open Innovations Association (FRUCT16). IEEE, 2014. http://dx.doi.org/10.1109/fruct.2014.7000941.
Повний текст джерелаAlcaraz, Juan J., Mario Lopez-Martinez, Javier Vales-Alonso, and Joan Garcia-Haro. "Background detection of primary user activity in Opportunistic Spectrum Access." In 2015 IEEE International Conference on Signal Processing for Communications (ICC). IEEE, 2015. http://dx.doi.org/10.1109/icc.2015.7248523.
Повний текст джерелаAvrahami, Daniel, Eveline van Everdingen, and Jennifer Marlow. "Supporting Multitasking in Video Conferencing using Gaze Tracking and On-Screen Activity Detection." In IUI'16: 21st International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2856767.2856801.
Повний текст джерелаCharoenkulvanich, Nathawan, Rie Kamikubo, Ryo Yonetani, and Yoichi Sato. "Assisting group activity analysis through hand detection and identification in multiple egocentric videos." In IUI '19: 24th International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3301275.3302297.
Повний текст джерелаChi, Yuhao, Lei Liu, Guanghui Song, Chau Yuen, Yong Liang Guan, and Ying Li. "Message Passing in C-RAN: Joint User Activity and Signal Detection." In 2017 IEEE Global Communications Conference (GLOBECOM 2017). IEEE, 2017. http://dx.doi.org/10.1109/glocom.2017.8254230.
Повний текст джерелаKumar, A. Sharath, and Sanjay Singh. "Detection of User Cluster with Suspicious Activity in Online Social Networking Sites." In 2013 2nd International Conference on Advanced Computing, Networking and Security (ADCONS). IEEE, 2013. http://dx.doi.org/10.1109/adcons.2013.17.
Повний текст джерелаBoljanovic, Veljko, Dejan Vukobratovic, Petar Popovski, and Cedomir Stefanovic. "User activity detection in massive random access: Compressed sensing vs. coded slotted ALOHA." In 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2017. http://dx.doi.org/10.1109/spawc.2017.8227652.
Повний текст джерелаЗвіти організацій з теми "In-app user activity detection"
Chen, Yona, Jeffrey Buyer, and Yitzhak Hadar. Microbial Activity in the Rhizosphere in Relation to the Iron Nutrition of Plants. United States Department of Agriculture, October 1993. http://dx.doi.org/10.32747/1993.7613020.bard.
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Повний текст джерелаSmit, Amelia, Kate Dunlop, Nehal Singh, Diona Damian, Kylie Vuong, and Anne Cust. Primary prevention of skin cancer in primary care settings. The Sax Institute, August 2022. http://dx.doi.org/10.57022/qpsm1481.
Повний текст джерелаGafny, Ron, A. L. N. Rao, and Edna Tanne. Etiology of the Rugose Wood Disease of Grapevine and Molecular Study of the Associated Trichoviruses. United States Department of Agriculture, September 2000. http://dx.doi.org/10.32747/2000.7575269.bard.
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Повний текст джерелаFluhr, Robert, and Maor Bar-Peled. Novel Lectin Controls Wound-responses in Arabidopsis. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7697123.bard.
Повний текст джерелаPerl-Treves, Rafael, Rebecca Grumet, Nurit Katzir, and Jack E. Staub. Ethylene Mediated Regulation of Sex Expression in Cucumis. United States Department of Agriculture, January 2005. http://dx.doi.org/10.32747/2005.7586536.bard.
Повний текст джерелаDelwiche, Michael, Boaz Zion, Robert BonDurant, Judith Rishpon, Ephraim Maltz, and Miriam Rosenberg. Biosensors for On-Line Measurement of Reproductive Hormones and Milk Proteins to Improve Dairy Herd Management. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7573998.bard.
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