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Relevant bibliographies by topics / Contextual anomalies

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Journal articles
Dissertations / Theses
Book chapters
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Academic literature on the topic 'Contextual anomalies'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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Journal articles on the topic "Contextual anomalies"

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Yu, Xiang, Hui Lu, Xianfei Yang, et al. "An adaptive method based on contextual anomaly detection in Internet of Things through wireless sensor networks." International Journal of Distributed Sensor Networks 16, no. 5 (2020): 155014772092047. http://dx.doi.org/10.1177/1550147720920478.

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With the widespread propagation of Internet of Things through wireless sensor networks, massive amounts of sensor data are being generated at an unprecedented rate, resulting in very large quantities of explicit or implicit information. When analyzing such sensor data, it is of particular importance to detect accurately and efficiently not only individual anomalous behaviors but also anomalous events (i.e. patterns of behaviors). However, most previous work has focused only on detecting anomalies while generally ignoring the correlations between them. Even in approaches that take into account

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Filik, Ruth. "Contextual override of pragmatic anomalies: Evidence from eye movements." Cognition 106, no. 2 (2008): 1038–46. http://dx.doi.org/10.1016/j.cognition.2007.04.006.

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Ngueilbaye, Alladoumbaye, Hongzhi Wang, Daouda Ahmat Mahamat, Ibrahim A. Elgendy, and Sahalu B. Junaidu. "Methods for detecting and correcting contextual data quality problems." Intelligent Data Analysis 25, no. 4 (2021): 763–87. http://dx.doi.org/10.3233/ida-205282.

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Knowledge extraction, data mining, e-learning or web applications platforms use heterogeneous and distributed data. The proliferation of these multifaceted platforms faces many challenges such as high scalability, the coexistence of complex similarity metrics, and the requirement of data quality evaluation. In this study, an extended complete formal taxonomy and some algorithms that utilize in achieving the detection and correction of contextual data quality anomalies were developed and implemented on structured data. Our methods were effective in detecting and correcting more data anomalies t

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Clausen, Henry, Gudmund Grov, and David Aspinall. "CBAM: A Contextual Model for Network Anomaly Detection." Computers 10, no. 6 (2021): 79. http://dx.doi.org/10.3390/computers10060079.

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Anomaly-based intrusion detection methods aim to combat the increasing rate of zero-day attacks, however, their success is currently restricted to the detection of high-volume attacks using aggregated traffic features. Recent evaluations show that the current anomaly-based network intrusion detection methods fail to reliably detect remote access attacks. These are smaller in volume and often only stand out when compared to their surroundings. Currently, anomaly methods try to detect access attack events mainly as point anomalies and neglect the context they appear in. We present and examine a

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Efanov, V. N., and N. S. Ivanova. "DIAGNOSTICS OF THE STATE OF COMPLEX TECHNICAL OBJECTS ON THE BASIS OF DETECTION OF ANOMALIES OF TIME SEQUENCES." Kontrol'. Diagnostika, no. 308 (February 2024): 56–62. http://dx.doi.org/10.14489/td.2024.02.pp.056-062.

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The problem of estimating the state of complex technical objects based on the analysis of time sequence anomalies is considered. The purpose of this research is to develop a method of detecting contextual anomalies of time sequences, which allows to determine the degree of development of degradation processes that lead to failures. The study of modern intellectual means of analyzing time sequences of high dimensionality is carried out. It is shown that contextual anomalies are of the greatest interest from the point of view of technical objects state estimation. We propose a spectral method fo

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Seymour, Deni J. "Contextual Incongruities, Statistical Outliers, and Anomalies: Targeting Inconspicuous Occupational Events." American Antiquity 75, no. 1 (2010): 158–76. http://dx.doi.org/10.7183/0002-7316.75.1.158.

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New methodologies are needed to address multiple componentcy and short reuse episodes that are characteristic of mobile group residential and logistical strategies. Chronometric results are often misinterpreted when evaluated within a framework suited to long-term sedentary occupations. The standard practices of age-averaging, eliminating apparent "anomalous" results, and relying on high profile diagnostic tools and vessels and the most visible features—along with the expectation for "contextual congruence"—mask multi-componentcy and episodic reuse. High incidences of site reuse have been dete

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Dou, Shaoyu, Kai Yang, and H. Vincent Poor. "PC2A: Predicting Collective Contextual Anomalies via LSTM With Deep Generative Model." IEEE Internet of Things Journal 6, no. 6 (2019): 9645–55. http://dx.doi.org/10.1109/jiot.2019.2930202.

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Wei, Ji Dong, and Ge Guo. "Multi-Sensor Stockline Tracking within a Blast Furnace." Applied Mechanics and Materials 701-702 (December 2014): 522–27. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.522.

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The paper presents a synergistic approach for height tracking within a blast furnace (BF). The Frequency Modulated Continuous Wave (FMCW) radar has been employed to measure the height and surface profile of the burden surface. However the radar signal is easily disturbed, by the radar anomalies, during the process of continuous measurement. The data from rotating chute and charging switch provide information on contextual relevance with radar anomalies. An anomaly detection models has been developed to increase the measurement accuracy by utilizing contextual information. The approach has been

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Al-Gabalawy, Mostafa. "Detecting anomalies within Unmanned Aerial Vehicle (UAV) video based on contextual saliency." Applied Soft Computing 96 (November 2020): 106715. http://dx.doi.org/10.1016/j.asoc.2020.106715.

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Frolova, E. A. "Typology of speech anomalies in the present-day advertising language." Russian language at school 84, no. 3 (2023): 68–76. http://dx.doi.org/10.30515/0131-6141-2023-84-3-68-76.

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The article considers different groups of speech anomalies found in modern advertising texts. The aim is threefold: to comprehensively analysis speech deviations in advertising texts, to develop a typology of speech anomalies and to determine their role in ensuring the communicative effectiveness of an advertising message. The study used analy sis (lexical, componential, word-formation, classification and differentiation), comparative and integrative methods, as well as the contextual-semantic analysis technique. These procedures enable identification of the content and semantic load of advert

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Dissertations / Theses on the topic "Contextual anomalies"

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Görnitz, Nico Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] [Müller, Manfred [Gutachter] Opper, and Marius [Gutachter] Kloft. "One-class classification in the presence of point, collective, and contextual anomalies / Nico Görnitz ; Gutachter: Klaus-Robert Müller, Manfred Opper, Marius Kloft ; Betreuer: Klaus-Robert Müller." Berlin : Technische Universität Berlin, 2019. http://d-nb.info/1178524663/34.

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Görnitz, Nico [Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] Müller, Manfred [Gutachter] Opper, and Marius [Gutachter] Kloft. "One-class classification in the presence of point, collective, and contextual anomalies / Nico Görnitz ; Gutachter: Klaus-Robert Müller, Manfred Opper, Marius Kloft ; Betreuer: Klaus-Robert Müller." Berlin : Technische Universität Berlin, 2019. http://d-nb.info/1178524663/34.

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Wilmet, Audrey. "Détection d'anomalies dans les flots de liens : combiner les caractéristiques structurelles et temporelles." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS402.

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Un flot de liens est un ensemble de liens {(t,u,v)} dans lequel un triplet (t,u,v) modélise l'interaction entre deux entités u et v à l'instant t. Dans de nombreuses situations, les données résultent de la mesure des interactions entre plusieurs millions d'entités au cours du temps et peuvent ainsi être étudiées grâce au formalisme des flots de liens. C'est le cas des appels téléphoniques, des échanges d'e-mails, des transferts d'argent, des contacts entre individus, du trafic IP, des achats en ligne, et bien d'autres encore. L'objectif de cette thèse est la détection d'ensembles de liens anor

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Vasco, Daniela Oliveira Baía Soares. "Identificação de Anomalias Contextuais." Master's thesis, 2013. https://repositorio-aberto.up.pt/handle/10216/70366.

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Vasco, Daniela Oliveira Baía Soares. "Identificação de Anomalias Contextuais." Dissertação, 2013. https://repositorio-aberto.up.pt/handle/10216/70366.

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Book chapters on the topic "Contextual anomalies"

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Vaudaine, Rémi, Baptiste Jeudy, and Christine Largeron. "Detection of Contextual Anomalies in Attributed Graphs." In Advances in Intelligent Data Analysis XIX. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74251-5_27.

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Corizzo, Roberto, Michelangelo Ceci, Gianvito Pio, Paolo Mignone, and Nathalie Japkowicz. "Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data." In Discovery Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88942-5_36.

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Baldoni, Sara, Giuseppe Celozzi, Alessandro Neri, Marco Carli, and Federica Battisti. "Inferring Anomaly Situation from Multiple Data Sources in Cyber Physical Systems." In Cyber-Physical Security for Critical Infrastructures Protection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69781-5_5.

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AbstractCyber physical systems are becoming ubiquitous devices in many fields thus creating the need for effective security measures. We propose to exploit their intrinsic dependency on the environment in which they are deployed to detect and mitigate anomalies. To do so, sensor measurements, network metrics, and contextual information are fused in a unified security architecture. In this paper, the model of the proposed framework is presented and a first proof of concept involving a telecommunication infrastructure case study is provided.

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Khrennikov, Andrei. "Contextual Approach to Quantum Theory." In Information Dynamics in Cognitive, Psychological, Social and Anomalous Phenomena. Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-94-017-0479-3_9.

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Okui, Norihiro, Shotaro Fukushima, Ayumu Kubota, and Takuya Yoshida. "Unsupervised Contextual Anomalous Communication Detection Using VQ Tokenization with Flow Data." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1624-4_7.

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Santhi, M., and Leya Elizabeth Sunny. "Contextual Multi-scale Region Convolutional 3D Network for Anomalous Activity Detection in Videos." In Computational Vision and Bio-Inspired Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37218-7_12.

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Zaverucha, Gerson. "A prioritized Contextual Default Logic: Curing anomalous extensions with a simple abnormality default theory." In KI-94: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58467-6_23.

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Wolff, Christian. "Composing Aseneth." In When Aseneth Met Joseph. Oxford University PressNew York, NY, 1998. http://dx.doi.org/10.1093/oso/9780195114751.003.0003.

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Abstract Ancient readers of Jewish scripture often found themselves confronted by textual anomalies, which they attempted to resolve. The means by which at least one circle of Jewish readers frequently did so is brilliantly illuminated in Kugel’s study of midrashic rabbinic traditions about Joseph and the wife of Potiphar, where he demonstrates their formulation and development as logical responses to textual and contextual anomalies and questions.

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Zadeh, Esmaeil, Stephen Amstutz, James Collins, Craig Ingham, Marian Gheorghe, and Savas Konur. "Automated Contextual Anomaly Detection for Network Interface Bandwidth Utilisation: A Case Study in Network Capacity Management." In Proceedings of CECNet 2021. IOS Press, 2021. http://dx.doi.org/10.3233/faia210459.

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We present a contextual anomaly detection methodology utilised for the capacity management process of a managed service provider that administers networks for large enterprises. We employ an ensemble of forecasts to identify anomalous network traffic. Stream of observations, upon their arrival, are compared against these baseline forecasts and alerts generated only if the anomalies are sustained. The results confirm that our approach significantly reduces false alerts, triggering rather more accurate and meaningful alerts to a level that could be proactively consumed by a small team. We believ

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Zhang, Xudong, Peng Peng, Xuewen Chen, Yulei Wu, Lingdong Shen, and Haina Tang. "STPDN: Spatio-Temporal Pattern Decomposition Network with Fluctuation Awareness for Robust Traffic Flow Forecasting." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240789.

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The significance of traffic prediction in modern urban life has become increasingly prominent. Accurate traffic forecasting improves urban traffic management and enhances road resource utilization. In recent years, many models have introduced spatio-temporal contextual embeddings to distinguish between different time steps and spatial nodes. However, these models often overlook anomalous fluctuations in traffic data due to data imbalance. Consequently, performance declines when encountering uncommon situations, especially those caused by unexpected traffic accidents. To maintain overall perfor

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Conference papers on the topic "Contextual anomalies"

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Yang, Zhengye, and Richard J. Radke. "Detecting Contextual Anomalies by Discovering Consistent Spatial Regions." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW). IEEE, 2025. https://doi.org/10.1109/wacvw65960.2025.00121.

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Emvoliadis, A., G. F. Angelis, A. Drosou, and D. Tzovaras. "Improving Air Quality Data Analysis by Injecting and Detecting Contextual Anomalies." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642679.

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Babai, Eilaf M. A., and Koji Okamura. "A Contextual Approach for Improving Anomalous Network Traffic Flows Prediction." In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2024. http://dx.doi.org/10.1109/compsac61105.2024.00353.

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Vasco, Daniela, Pedro Pereira Rodrigues, and Joao Gama. "Contextual anomalies in medical data." In 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2013. http://dx.doi.org/10.1109/cbms.2013.6627869.

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Prado-Romero, Mario Alfonso, and Andres Gago-Alonso. "Detecting contextual collective anomalies at a Glance." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900017.

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Dimopoulos, Giorgos, Pere Barlet-Ros, Constantine Dovrolis, and Ilias Leontiadis. "Detecting network performance anomalies with contextual anomaly detection." In 2017 IEEE International Workshop on Measurements & Networking (M&N). IEEE, 2017. http://dx.doi.org/10.1109/iwmn.2017.8078404.

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Latif, Hamid, José Suárez-Varela, Albert Cabellos-Aparicio, and Pere Barlet-Ros. "Detecting Contextual Network Anomalies with Graph Neural Networks." In CoNEXT 2023: The 19th International Conference on emerging Networking EXperiments and Technologies. ACM, 2023. http://dx.doi.org/10.1145/3630049.3630171.

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Carmona, Chris U., François-Xavier Aubet, Valentin Flunkert, and Jan Gasthaus. "Neural Contextual Anomaly Detection for Time Series." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/394.

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We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series that scales seamlessly from the unsupervised to supervised setting, and is applicable to both univariate and multivariate time series. This is achieved by combining recent developments in representation learning for multivariate time series, with techniques for deep anomaly detection originally developed for computer vision that we tailor to the time series setting. Our window-based approach facilitates learning the boundary between normal and anomalous classes by injecting generic synthet

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Fan Jiang, Ying Wu, and Aggelos K. Katsaggelos. "Detecting contextual anomalies of crowd motion in surveillance video." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5414535.

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Sharma, Shivam, Mirdul Swarup, Tanush Mahajan, and Zeel Dilipkumar Patel. "Detecting anomalies, contradictions, and contextual analysis through NLP in text." In 2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). IEEE, 2022. http://dx.doi.org/10.1109/icict55121.2022.10064560.

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