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Thematische Bibliographien / User activity detection

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Zeitschriftenartikel
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Bücher
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Auswahl der wissenschaftlichen Literatur zum Thema „User activity detection“

Autor: Grafiati

Veröffentlicht am 11. Januar 2025

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Zeitschriftenartikel zum Thema "User activity detection"

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Zhu, Hao, and Georgios B. Giannakis. "Exploiting Sparse User Activity in Multiuser Detection." IEEE Transactions on Communications 59, no. 2 (2011): 454–65. http://dx.doi.org/10.1109/tcomm.2011.121410.090570.

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Mitra, 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.

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Lee, Junho, and Seung-Hwan Lee. "Low dimensional multiuser detection exploiting low user activity." Journal of Communications and Networks 15, no. 3 (2013): 283–91. http://dx.doi.org/10.1109/jcn.2013.000051.

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4

Zou, Shihong, Huizhong Sun, Guosheng Xu, and Ruijie Quan. "Ensemble Strategy for Insider Threat Detection from User Activity Logs." Computers, Materials & Continua 65, no. 2 (2020): 1321–34. http://dx.doi.org/10.32604/cmc.2020.09649.

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Wang, Shuwen, Xingquan Zhu, Weiping Ding, and Amir Alipour Yengejeh. "Cyberbullying and Cyberviolence Detection: A Triangular User-Activity-Content View." IEEE/CAA Journal of Automatica Sinica 9, no. 8 (2022): 1384–405. http://dx.doi.org/10.1109/jas.2022.105740.

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Park, Hansol, Kookjin Kim, Dongil Shin, and Dongkyoo Shin. "BGP Dataset-Based Malicious User Activity Detection Using Machine Learning." Information 14, no. 9 (2023): 501. http://dx.doi.org/10.3390/info14090501.

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Recent advances in the Internet and digital technology have brought a wide variety of activities into cyberspace, but they have also brought a surge in cyberattacks, making it more important than ever to detect and prevent cyberattacks. In this study, a method is proposed to detect anomalies in cyberspace by consolidating BGP (Border Gateway Protocol) data into numerical data that can be trained by machine learning (ML) through a tokenizer. BGP data comprise a mix of numeric and textual data, making it challenging for ML models to learn. To convert the data into a numerical format, a tokenizer

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Parwez, 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 (2017): 2058–65. http://dx.doi.org/10.1109/tii.2017.2650206.

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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 (2022): 67. http://dx.doi.org/10.3390/fi14020067.

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In this study, a simple yet effective framework is proposed to characterize fine-grained in-app user activities performed on mobile applications using a convolutional neural network (CNN). The proposed framework uses a time window-based approach to split the activity’s encrypted traffic flow into segments, so that in-app activities can be identified just by observing only a part of the activity-related encrypted traffic. In this study, matrices were constructed for each encrypted traffic flow segment. These matrices acted as input into the CNN model, allowing it to learn to differentiate previ

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Bashir, 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 (2017): 157–75. http://dx.doi.org/10.1108/ijpcc-03-2017-0027.

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Purpose This purpose of this paper is to develop a change detection technique for activity recognition model. The approach aims to detect changes in the initial accuracy of the model after training and when the model is deployed for recognizing new unseen activities without access to the ground truth. The changes between the two sessions may occur because of differences in sensor placement, orientation and user characteristics such as age and gender. However, many of the existing approaches for model adaptation in activity recognition are blind methods because they continuously adapt the recog

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Kim, Park, Kim, Cho, and Kang. "Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms." Applied Sciences 9, no. 19 (2019): 4018. http://dx.doi.org/10.3390/app9194018.

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Insider threats are malicious activities by authorized users, such as theft of intellectual property or security information, fraud, and sabotage. Although the number of insider threats is much lower than external network attacks, insider threats can cause extensive damage. As insiders are very familiar with an organization’s system, it is very difficult to detect their malicious behavior. Traditional insider-threat detection methods focus on rule-based approaches built by domain experts, but they are neither flexible nor robust. In this paper, we propose insider-threat detection methods based

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Dissertationen zum Thema "User activity detection"

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Amanzi, Richard. "A natural user interface architecture using gestures to facilitate the detection of fundamental movement skills." Thesis, Nelson Mandela Metropolitan University, 2015. http://hdl.handle.net/10948/6204.

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Fundamental movement skills (FMSs) are considered to be one of the essential phases of motor skill development. The proper development of FMSs allows children to participate in more advanced forms of movements and sports. To be able to perform an FMS correctly, children need to learn the right way of performing it. By making use of technology, a system can be developed that can help facilitate the learning of FMSs. The objective of the research was to propose an effective natural user interface (NUI) architecture for detecting FMSs using the Kinect. In order to achieve the stated objective, an

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Sagheer, Fakher. "Bayesian statistical methods for joint user activity detection, channel estimation, and data decoding in dynamic wireless networks." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. https://theses.hal.science/tel-04874844.

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L'accès multiple non-orthogonal grant-free (GF-NOMA) s'impose progressivement comme une partie intégrante de la couche physique des systèmes d'accès radio du futur. En permettant d'accéder à une station de base sans allocation explicite de ressources temps/fréquence/code, GF-NOMA permet non seulement d'améliorer l'efficacité spectrale, mais également de rendre possible des communications ultra fiables à faible latence (URLLC). De telles exigences permettront de répondre aux enjeux spécifiques d'applications sans fil telles que l'internet des objets, la réalité virtuelle, les jeux vidéo en lign

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Dias, Pereira dos Santos Augusto. "Using Motion Sensor and Machine Learning to Support the Assessment of Rhythmic Skills in Social Partner Dance: Bridging Teacher, Student and Machine Contexts." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21302.

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Social partner dance is a popular form of physical activity intended for enjoyment and pleasure. Rhythm is an essential aspect of dance, especially in relation to music; however, it can be a difficult psychomotor skill to learn. Rhythm learning is hindered because dance teachers have a short time during classes to assess and provide feedback to students. Current technical solutions enable automatic assessment and feedback for dance students; however, the available technology is either expensive or cumbersome. Recent improvement and decreased price of wearable technology offer new opportunities

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Odehnal, Jiří. "Řízení a měření sportovních drilů hlasem/zvuky." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-399705.

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This master's thesis deals with the design and development of mobile aplication for Android platform. The aim of the work is to implement a simple and user-friendly user interface that would support and assist the user in trainning and sport exercises. The thesis also include implementation of sound detection to support during exercises and voice instruction by application. In practice the application should help in making training exercises more comfortable without the user being forced to keep mobile device in hand.

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McEachern, Matthew. "Neural Voice Activity Detection and its practical use." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119733.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 87-90).<br>The task of producing a Voice Activity Detector (VAD) that is robust in the presence of non-stationary background noise has been an active area of research for several decades. Historically, many of the proposed V

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Tholen, Andrea. "The use of animal activity data and milk components as indicators of clinical mastitis." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/76826.

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A study was conducted to examine the correlation between a novel behavior monitoring system and a validated data logger. We concluded that the behavior monitoring system was valid for tracking daily rest time in dairy cows (R=0.96); however the correlation values for rest bouts and rest duration were relatively low, (R=0.64) (R=0.47), respectively. Daily monitoring of animal activity and milk components can be used to detect mastitis prior to clinical onset. Data from 268 cases with clinical mastitis and respective controls (n=268) from Virginia Tech and the University of Florida dairy herd

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Azhar, Faisal. "Marker-less human body part detection, labelling and tracking for human activity recognition." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/69575/.

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This thesis focuses on the development of a real-time and cost eﬀective marker-less computer vision method for significant body point or part detection (i.e., the head, arm, shoulder, knee, and feet), labelling and tracking, and its application to activity recognition. This work comprises of three parts: significantbody point detection and labelling, significant body point tracking, and activity recognition. Implicit body models are proposed based on human anthropometry, kinesiology, and human vision inspired criteria to detect and label significant body points. The key idea of the proposed me

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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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Many military conflicts are fought in urban environments that subject the U.S. soldier to a number of challenges not otherwise found in traditional battle. In the urban environment, the soldier is subject to threatening attacks not only from the organized army but also from civilians who harbor hostility. U.S. enemies use the civilian crowd as an unconventional tactic to blend in and look like civilians, and in response to this growing trend, soldiers must detect and identify civilians as a threat or non-threat. To identify a civilian as a threat, soldiers must familiarize themselves with b

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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.

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In this work, a coordinate wise descent algorithm is implemented which serves the purpose of estimating active users in a base station/client wireless communication setup. The implemented algorithm utilizes the sporadic nature of users, which is believed to be the norm with 5G Massive MIMO and Internet of Things, meaning that only a subset of all users are active simultaneously at any given time. This work attempts to estimate the viability of a direct algorithm implementation to test if the performance requirements can be satisfied or if a more sophisticated implementation, such as a parallel

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Goodlich, Benjamin I. "Machine learning algorithms for the automatic detection and classification of physical activity in children with cerebral palsy who use mobility aids for ambulation." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/389853.

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Background: Literature related to objective measurement of habitual physical activity (PA) disproportionately over represents children with Cerebral Palsy (CP) who are ambulant. Consequently, it is unknown if methods used to examine PA, such as machine learning models built on accelerometer data, are able to accurately detect PA in children with CP who use mobility aids for ambulation. Objective: To develop and test machine learning models used for the automatic detection and classification of PA type in children with CP who use mobility aids for ambulation. Methods: Eleven children and adoles

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Bücher zum Thema "User activity detection"

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Nazarov, Dmitriy, and Anton Kopnin. Information technologies in professional activity: data mining and business analytics. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2110964.

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The textbook offers a comprehensive look covering a wide range of topics related to the role of information technology in the modern world. Starting with an overview of the history of information technology and its evolution, the book introduces the reader to professions in the field of information and digital technologies, emphasizing them in various professional fields. The content of the textbook includes a detailed analysis of key concepts such as Data Science, Data Mining and Machine Learning, and their role in healthcare, law, education, science and business. The textbook provides a deta

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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.

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The use of physiological signals to detect deception can be traced back almost a century. Historically, the polygraph has been used—and debated. This chapter discusses the merits of polygraph testing, and to what extent the introduction of measures of brain activity—most notably functional magnetic imaging (fMRI)—have solved the problems associated with polygraph testing. It discusses the different question formats used with polygraph and brain activity measures, and argues that these formats are the main factor contributing to the tests’ validity. Moreover, the authors argue that erroneous te

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Shaikh, 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.

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Does your brain replay your recent life experiences while you are resting? An open question in neuroscience is which events does our brain replay and is there any correlation between the replay and duration of the event? In this study I tried to investigate this question by using Magnetoencephalography data from an active listening experiment. Magnetoencephalography (MEG) is a non-invasive neuroimaging technique used to study the brain activity and understand brain dynamics in perception and cognitive tasks particularly in the fields of speech and hearing. It records the magnetic field generat

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Ourada, 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.

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Active abuse of substances by inmates poses a challenge for correctional psychiatrists. Substance use disorders (SUD) are common among inmates, with higher prevalence usually found in those with general psychiatric conditions. Knowledge about substance use in correctional facilities fosters competent clinical intervention and enhances management at all levels. Psychiatrists working in jails and prisons have the challenging task of maintaining therapeutic alliances with patients who have co-occurring SUDs and also may be actively using substances. Patients might not spontaneously report use dur

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Walczak, 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.

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In the paper discussed in this chapter, Ainsley Iggo used electrophysiology to characterize mechanosensory fibres from the saphenous nerve in cats. Using fine techniques of dissection he recorded from single units and therefore could discriminate between the various types of sensitivity of afferent fibres. This article describes properties of primary afferent neurons in response to precise calibrated mechanical stimuli and focused on mechanical sensitivity of C-fibres. In addition, the manuscript describes the properties of skin-receptor fields. The paper showed that not all C-fibres responded

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Chinoy, Hector, and Robert G. Cooper. Polymyositis and dermatomyositis. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0124.

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Polymyositis (PM), dermatomyositis (DM), and inclusion body myositis (IBM) form part of the idiopathic inflammatory myopathies (IIM), a heterogeneous group of rare autoimmune diseases characterized by an acquired proximal muscle weakness, raised muscle enzymes (including creatine kinase), inflammatory cell infiltrates in muscle biopsy tissue, electrophysiological abnormalities, and presence of circulating myositis-specific/myositis-associated autoantibodies. The underlying aetiology of IIM is poorly understood, but likely involves interactions between environmental and genetic risk factors. My

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Harper, 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.

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The goals of treatment in renal vasculitis are to stop vasculitic activity and recover renal function. Subsequent strategies are required to prevent vasculitis returning and to address longer-term co-morbidities caused by tissue damage, drug toxicity, and increased cardiovascular and malignancy risk.Cyclophosphamide and high-dose glucocorticoids remain the standard induction therapy with alternative immunosuppressives, such as azathioprine, to prevent relapse. Plasma exchange improves renal recovery in severe presentations. Refractory disease resulting from a failure of induction or remission

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Ufimtseva, 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.

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The monograph reflects the problems of Russian psycholinguistics from the moment of its inception in Russia to the present day and presents its main directions that are currently developing. In addition, theoretical developments and practical results obtained in the framework of different directions and research centers are described in a concise form. The task of the book is to reflect, as far as it is possible in one edition, firstly, the history of the formation of Russian psycholinguistics; secondly, its methodology and developed methods; thirdly, the results obtained in different research

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Buchteile zum Thema "User activity detection"

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Baek, Jonghun, Geehyuk Lee, Wonbae Park, and Byoung-Ju Yun. "Accelerometer Signal Processing for User Activity Detection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30134-9_82.

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Jahn, Andreas, Marek Bachmann, Philipp Wenzel, and Klaus David. "Focus on the User: A User Relative Coordinate System for Activity Detection." In Modeling and Using Context. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57837-8_47.

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Scardino, Giuseppe, Ignazio Infantino, and Filippo Vella. "Recognition of Human Identity by Detection of User Activity." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39345-7_6.

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Miu, TungNgai, Chenxu Wang, Daniel Xiapu Luo, and Jinhe Wang. "Modeling User Browsing Activity for Application Layer DDoS Attack Detection." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59608-2_42.

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Grushka-Cohen, Hagit, Ofer Biller, Oded Sofer, Lior Rokach, and Bracha Shapira. "Simulating User Activity for Assessing Effect of Sampling on DB Activity Monitoring Anomaly Detection." In Policy-Based Autonomic Data Governance. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17277-0_5.

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Märker, 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. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13105-4_4.

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Tokhtabayev, 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. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65127-9_17.

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Lukashin, 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. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6632-9_28.

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Burghouwt, Pieter, Marcel Spruit, and Henk Sips. "Towards Detection of Botnet Communication through Social Media by Monitoring User Activity." In Information Systems Security. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25560-1_9.

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Ortega, 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. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33353-3_9.

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Konferenzberichte zum Thema "User activity detection"

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Derya, Sertac, Shih-Chen Yu, Hsu-Wen Young, Eduard A. Jorswieck, Pin-Hsun Lin, and Shih-Chun Lin. "Joint Delay And User Activity Detection in Asynchronous Massive Access." In 2024 33rd Wireless and Optical Communications Conference (WOCC). IEEE, 2024. https://doi.org/10.1109/wocc61718.2024.10786075.

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Sun, Guangyue, Zhaoji Zhang, and Ying Li. "Hybrid Model-Data-Driven User-Activity Detection Network for Massive Random Access." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683656.

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Zhang, Yuanyuan, Yongtian Luo, Jin Xu, Weihua Liu, Lixun Huang, and Zhe Zhang. "Structured Compressive Sensing Based Joint User Activity and Data Detection for Grant-Free mMTC." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743465.

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Khan, Muhammad Usman, Enrico Testi, Marco Chiani, and Enrico Paolini. "Blind User Activity Detection for Grant-Free Random Access in Cell-Free mMIMO Networks." In 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI). IEEE, 2024. http://dx.doi.org/10.1109/rtsi61910.2024.10761492.

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Forsch, Christian, Alexander Karataev, and Laura Cottatellucci. "Distributed Joint User Activity Detection, Channel Estimation, and Data Detection via Expectation Propagation in Cell-Free Massive MIMO." In 2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2024. http://dx.doi.org/10.1109/spawc60668.2024.10694527.

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Wang, Yixin, Yichen Wang, Tao Wang, and Julian Cheng. "Joint Channel Estimation and User Activity Detection for mmWave Grant-Free Massive MTC Networks Under Pilot Contamination Attack." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683251.

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Zhong, Sheng, Li Zhen, Shuchang Li, Ping Dong, and Hao Qin. "Joint User Activity Detection and Channel Estimation for Grant-Free Temporal-Correlated Random Access in LEO Satellite Based IoT." In 2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceict61637.2024.10671064.

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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. ACM, 2021. http://dx.doi.org/10.1145/3478301.3478303.

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Swarnalaxmi, 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.

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Hu, 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.

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Berichte der Organisationen zum Thema "User activity detection"

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Malik, Arun, Andrea López, and Paul E. Carrillo. Pollution or Crime: The Effect of Driving Restrictions on Criminal Activity. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0011747.

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Driving restriction programs have been implemented in many cities around the world to alleviate pollution and congestion problems. Enforcement of such programs is costly and can potentially displace policing resources used for crime prevention and crime detection. Hence, driving restrictions may increase crime. To test this hypothesis, this paper exploits both temporal and spatial variation in the implementation of Quito, Ecuador's Pico y Placa program and evaluates its effect on crime. Both difference-in-difference and spatial regression discontinuity estimates provide credible evidence that

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Michaels, Trevor. Red-tailed boa (Boa constrictor) surveys at Salt River Bay National Park, St. Croix U.S. Virgin Islands: 2023 report of activities. National Park Service, 2024. http://dx.doi.org/10.36967/2303799.

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St. Croix is home to a variety of threatened and endangered (T&E) species that are at risk for predation by the invasive red-tailed boa (Boa constrictor), such as the St. Croix ground lizard (Amevia polyps), the ground-nesting least tern (Sterna antillarum), and the hawksbill sea turtle (Eretmochelys imbricata). Genetic analysis determined the original red-tailed boa population on St. Croix sourced from a single female released by a pet owner and its range expands every year. Presently, the main population of red-tailed boa is established on the west end of St. Croix and extends as far eas

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Barefoot, Susan F., Bonita A. Glatz, Nathan Gollop, and Thomas A. Hughes. Bacteriocin Markers for Propionibacteria Gene Transfer Systems. United States Department of Agriculture, 2000. http://dx.doi.org/10.32747/2000.7573993.bard.

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The antibotulinal baceriocins, propionicin PLG-1 and jenseniin G., were the first to be identified, purified and characterized for the dairy propionibaceria and are produced by Propionibacterium thoenii P127 and P. thoenii/jensenii P126, respectively. Objectives of this project were to (a) produce polyclonal antibodies for detection, comparison and monitoring of propionicin PLG-1; (b) identify, clone and characterize the propionicin PLG-1 (plg-1) and jenseniin G (jnG) genes; and (3) develop gene transfer systems for dairy propionibacteria using them as models. Polyclonal antibodies for detecti

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Cytryn, 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, 2014. http://dx.doi.org/10.32747/2014.7598174.bard.

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Control of agro-associated pathogens is becoming increasingly difficult due to increased resistance and mounting restrictions on chemical pesticides and antibiotics. Likewise, in veterinary and human environments, there is increasing resistance of pathogens to currently available antibiotics requiring discovery of novel antibiotic compounds. These drawbacks necessitate discovery and application of microorganisms that can be used as biocontrol agents (BCAs) and the isolation of novel biologically-active compounds. This highly-synergistic one year project implemented an innovative pipeline aimed

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Hanna, Benjamin, Tom Bubenik, and Barbara Padgett. PR186-203813-R01 Literature Review Pipeline Mid-wall Defect Detection and FFS Assessment. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0012076.

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A literature review on the current industry understanding of mid-wall cracking and its detection was conducted. A summary of key factors influencing mid-wall cracking and modern integrity management approaches for the phenomenon are summarized. Based on the literature review and subject matter expert interviews, gaps in the published industry knowledge were identified. These gaps will be used to drive work in the direction of backfilling the valuable information that may be used to mitigate the onset of mid-wall cracking and to identify the presence of mid-wall cracking so that the threat can

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Wongpakdeea, Thinnapong, Karin Crenshaw, Hery Figueroa Wong, Duangjai Nacapricha, and Bruce McCord. Advancements in Analytical Techniques for Rapid Identification of Gunshot Residue and Low Explosives through Electrochemical Detection and Surface-Enhanced Raman Spectroscopy. Florida International University, 2024. https://doi.org/10.25148/gfjcsr.2024.7.

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This research focuses on developing two analytical methods for forensic investigations using electrochemical detection and surface-enhanced Raman spectroscopy. For electrochemical analysis, screen-printed carbon electrodes are used to detect metals and nitrate/nitrite compounds commonly found in gunshot residue. Gold electrodeposition and copper modification enhance sensitivity and catalytic activity, respectively. Additionally, a screen-printed gold electrode modified with gold nanoparticles enables surface-enhanced Raman spectroscopy, requiring only a single drop of sample solution. Testing

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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, 1993. http://dx.doi.org/10.32747/1993.7613020.bard.

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Iron is the fourth most abundant element in the soil, but since it forms insoluble hydroxides at neutral and basic pH, it often falls short of meeting the basic requirements of plants and microorganisms. Most aerobic and facultative aerobic microorganisms possess a high-affinity Fe transport system in which siderophores are excreted and the consequent Fe complex is taken up via a cognate specific receptor and a transport pathway. The role of the siderophore in Fe uptake by plants and microorganisms was the focus of this study. In this research Rhizopus arrhizus was found to produce a novel sid

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Burns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.

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The Genetic Technology (Precision Breeding) Act (England) aims to develop a science-based process for the regulation and authorisation of precision bred organisms (PBOs). PBOs are created by genetic technologies but exhibit changes which could have occurred through traditional processes. This current review, commissioned by the Food Standards Agency (FSA), aims to clarify existing terminologies, explore viable methods for the detection, identification, and quantification of products of precision breeding techniques, address and identify potential solutions to the analytical challenges presente

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Graham, Timothy, and Katherine M. FitzGerald. Bots, Fake News and Election Conspiracies: Disinformation During the Republican Primary Debate and the Trump Interview. Queensland University of Technology, 2023. http://dx.doi.org/10.5204/rep.eprints.242533.

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We used Alexandria Digital, a world leading disinformation detection technology, to analyse almost a million posts on X (formerly known as Twitter) and Reddit comments during the first Republican primary debate and counterprogrammed Tucker Carlson and Donald Trump interview on the 23rd of August. What we did: • Collected 949,259 posts from the platform X, formerly known as Twitter. These posts were collected if they contained one of 11 relevant hashtags or keywords and were posted between 8:45pm and 11:15pm EST on 23rd August 2023. • Collected 20,549 comments from two separate Reddit threads.

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Zárate-Solano, Héctor M., and Norberto Rodríguez-Niño. Consumer Prices Trends in Colombia: Detecting Breaks and Forecasting Inflation. Banco de la República, 2024. https://doi.org/10.32468/be.1289.

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Colombia’s annual inflation reached 13.3% in March 2023, the highest rate since the implementation of the inflation-targeting regime for monetary policy in 2000. However, some groups within the basket show signs of lower inflation, while others exhibit higher inflation. The persistence of this trend is actively debated, involving analysis of both year-to-year and month-to-month changes in price indices. In this paper, we use a time series methodology to identify shifts in inflation levels based on the 188 price indices that comprise the basket. We classify the trend breaks as positive or negat

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