Academic literature on the topic 'In-app user activity detection'
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Journal articles on the topic "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.
Full textPathmaperuma, 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.
Full textZhu, 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.
Full textMitra, 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.
Full textKim, 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.
Full textParwez, 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.
Full textBashir, 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.
Full textLee, 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.
Full textZain 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.
Full textKarthikeyan, 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.
Full textDissertations / Theses on the topic "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.
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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.
Full textHuang, 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.
Full textWeaver, 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.
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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.
Full textDetection 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.
Books on the topic "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.
Full textShaikh, 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.
Full textOurada, 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.
Full textWalczak, 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.
Full textUfimtseva, 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.
Full textChinoy, Hector, and Robert G. Cooper. Polymyositis and dermatomyositis. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0124.
Full textHarper, 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.
Full textBook chapters on the topic "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.
Full textScardino, 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.
Full textTokhtabayev, 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.
Full textMärker, 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.
Full textLukashin, 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.
Full textAhmed, 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.
Full textOrtega, 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.
Full textFlores-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.
Full textZeng, 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.
Full textKleanthous, 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.
Full textConference papers on the topic "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.
Full textSwarnalaxmi, 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.
Full textHu, 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.
Full textMarchenkov, 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.
Full textAlcaraz, 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.
Full textAvrahami, 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.
Full textCharoenkulvanich, 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.
Full textChi, 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.
Full textKumar, 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.
Full textBoljanovic, 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.
Full textReports on the topic "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.
Full textCytryn, Eddie, Mark R. Liles, and Omer Frenkel. Mining multidrug-resistant desert soil bacteria for biocontrol activity and biologically-active compounds. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598174.bard.
Full textBarefoot, Susan F., Bonita A. Glatz, Nathan Gollop, and Thomas A. Hughes. Bacteriocin Markers for Propionibacteria Gene Transfer Systems. United States Department of Agriculture, June 2000. http://dx.doi.org/10.32747/2000.7573993.bard.
Full textSessa, Guido, and Gregory Martin. role of FLS3 and BSK830 in pattern-triggered immunity in tomato. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7604270.bard.
Full textSmit, 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.
Full textGafny, 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.
Full textMizrach, Amos, Michal Mazor, Amots Hetzroni, Joseph Grinshpun, Richard Mankin, Dennis Shuman, Nancy Epsky, and Robert Heath. Male Song as a Tool for Trapping Female Medflies. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7586535.bard.
Full textFluhr, 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.
Full textPerl-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.
Full textDelwiche, 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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