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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 correlations between anomalies, most disregard the fact that the anomaly status of sensor data changes over time. In this article, we propose an unsupervised contextual anomaly detection method in Internet of Things through wireless sensor networks. This method accounts for both a dynamic anomaly status and correlations between anomalies based contextually on their spatial and temporal neighbors. We then demonstrate the effectiveness of the proposed method in an anomaly detection model. The experimental results show that this method can accurately and efficiently detect not only individual anomalies but also anomalous events.
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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 than existing taxonomy techniques, and also highlighted the demerit of Support Vector Machine (SVM). These proposed techniques, therefore, will be of relevance in detection and correction of errors in large contextual data (Big data).
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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 contextual bidirectional anomaly model (CBAM) based on deep LSTM-networks that is specifically designed to detect such attacks as contextual network anomalies. The model efficiently learns short-term sequential patterns in network flows as conditional event probabilities. Access attacks frequently break these patterns when exploiting vulnerabilities, and can thus be detected as contextual anomalies. We evaluated CBAM on an assembly of three datasets that provide both representative network access attacks, real-life traffic over a long timespan, and traffic from a real-world red-team attack. We contend that this assembly is closer to a potential deployment environment than current NIDS benchmark datasets. We show that, by building a deep model, we are able to reduce the false positive rate to 0.16% while effectively detecting six out of seven access attacks, which is significantly lower than the operational range of other methods. We further demonstrate that short-term flow structures remain stable over long periods of time, making the CBAM robust against concept drift.
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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 detected by considering alternate site development models and looking specifically for evidence of distinct shorter term occupations.
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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 validated on real BF. The root mean squared (RMS) error in the measured height is reduced by 17% when using the proposed approach compared to the case without it. The results suggest that the proposed approach successfully adapts to changes in the pattern and characteristics of the burden surface.
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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 advertising text components. Drawing on relevant scientific literature, I communicate my own vision of the typology of speech anomalies. The paper explains a rationale for considering the inter-step word-formation as the basis for creating speech anomalies and the interaction of elements of different language levels as a condition for the appearance of deviations.
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Zhao, Bo, Xiang Li, Jiayue Li, Jianwen Zou, and Yifan Liu. "An Area-Context-Based Credibility Detection for Big Data in IoT." Mobile Information Systems 2020 (January 25, 2020): 1–12. http://dx.doi.org/10.1155/2020/5068731.
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In order to improve the credibility of big data analysis platform’s results in IoT, it is necessary to improve the quality of IoT data. Many detection methods have been proposed to filter out incredible data, but there are certain deficiencies that performance is not high, detection is not comprehensive, and process is not credible. So this paper proposes an area-context-based credibility detection method for IoT data, which can effectively detect point anomalies, behavioral anomalies, and contextual anomalies. The performance of the context determination and the data credibility detection of the device satisfying the area characteristics is superior to the similar algorithms. As the experiments show, the proposed method can reach a high level of performance with more than 97% in metrics, which can effectively improve the quality of IoT data.
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Boukela, Lynda, Gongxuan Zhang, Meziane Yacoub, Samia Bouzefrane, and Sajjad Bagheri Baba Ahmadi. "An approach for unsupervised contextual anomaly detection and characterization." Intelligent Data Analysis 26, no. 5 (2022): 1185–209. http://dx.doi.org/10.3233/ida-215906.
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Outlier detection has been widely explored and applied to different real-world problems. However, outlier characterization that consists in finding and understanding the outlying aspects of the anomalous observations is still challenging. In this paper, we present a new approach to simultaneously detect subspace outliers and characterize them. We introduce the Dimension-wise Local Outlier Factor (DLOF) function to quantify the degree of outlierness of the data points in each feature dimension. The obtained DLOFs are used in an outlier ensemble so as to detect and rank the anomalous points. Subsequently, the same DLOFs are analyzed in order to characterize the detected outliers with their relevant subspace and their same-type anomalies. Experiments on various datasets show the efficacy of our method. Indeed, we demonstrate through an experimental evaluation that the proposed approach is competitive compared to the existing solutions in terms of both detection and characterization accuracy.
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Dias, Maurício Araújo, Giovanna Carreira Marinho, Rogério Galante Negri, Wallace Casaca, Ignácio Bravo Muñoz, and Danilo Medeiros Eler. "A Machine Learning Strategy Based on Kittler’s Taxonomy to Detect Anomalies and Recognize Contexts Applied to Monitor Water Bodies in Environments." Remote Sensing 14, no. 9 (2022): 2222. http://dx.doi.org/10.3390/rs14092222.
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Environmental monitoring, such as analyses of water bodies to detect anomalies, is recognized worldwide as a task necessary to reduce the impacts arising from pollution. However, the large number of data available to be analyzed in different contexts, such as in an image time series acquired by satellites, still pose challenges for the detection of anomalies, even when using computers. This study describes a machine learning strategy based on Kittler’s taxonomy to detect anomalies related to water pollution in an image time series. We propose this strategy to monitor environments, detecting unexpected conditions that may occur (i.e., detecting outliers), and identifying those outliers in accordance with Kittler’s taxonomy (i.e., detecting anomalies). According to our strategy, contextual and non-contextual image classifications were semi-automatically compared to find any divergence that indicates the presence of one type of anomaly defined by the taxonomy. In our strategy, models built to classify a single image were used to classify an image time series due to domain adaptation. The results 99.07%, 99.99%, 99.07%, and 99.53% were achieved by our strategy, respectively, for accuracy, precision, recall, and F-measure. These results suggest that our strategy allows computers to recognize contexts and enhances their capabilities to solve contextualized problems. Therefore, our strategy can be used to guide computational systems to make different decisions to solve a problem in response to each context. The proposed strategy is relevant for improving machine learning, as its use allows computers to have a more organized learning process. Our strategy is presented with respect to its applicability to help monitor environmental disasters. A minor limitation was found in the results caused by the use of domain adaptation. This type of limitation is fairly common when using domain adaptation, and therefore has no significance. Even so, future work should investigate other techniques for transfer learning.
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Chang, Ruohan, Xiaohong Yang, and Yufang Yang. "Processing good-fit anomalies is modulated by contextual accessibility during discourse comprehension: ERP evidence." Language, Cognition and Neuroscience 35, no. 10 (2020): 1423–34. http://dx.doi.org/10.1080/23273798.2020.1784448.
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Xu, Liyan, Kang Xu, Yinchuan Qin, et al. "TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data." Applied Sciences 12, no. 16 (2022): 8085. http://dx.doi.org/10.3390/app12168085.
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Anomaly detection on time series data has been successfully used in power grid operation and maintenance, flow detection, fault diagnosis, and other applications. However, anomalies in time series often lack strict definitions and labels, and existing methods often suffer from the need for rigid hypotheses, the inability to handle high-dimensional data, and highly time-consuming calculation costs. Generative Adversarial Networks (GANs) can learn the distribution pattern of normal data, detecting anomalies by comparing the reconstructed normal data with the original data. However, it is difficult for GANs to extract contextual information from time series data. In this paper, we propose a new method, Transformer-based GAN for Anomaly Detection of Time Series Data (TGAN-AD), The transformer-based generators of TGAN-AD can extract contextual features of time series data to prompt the performance. TGAN-AD’s discriminator can also assist in determining abnormal data. Anomaly scores are calculated through both the generator and the discriminator. We have conducted comprehensive experiments on three public datasets. Experimental results show that our TGAN-AD has better performance in anomaly detection than the state-of-the-art anomaly detection techniques, with the highest Recall and F1 values on all datasets. Our experiments also demonstrate the high efficiency of the model and the optimal choice of hyperparameters.
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Suryadi, Taufik, Kumalasari Kumalasari, and Kulsum Kulsum. "Ethical and Medicolegal Considerations in the Termination of Pregnancy Due to Lethal Congenital Anomalies in Banda Aceh, Indonesia." Open Access Macedonian Journal of Medical Sciences 8, no. C (2020): 167–71. http://dx.doi.org/10.3889/oamjms.2020.5085.
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BACKGROUND: The aim of the following paper is to present the case termination of pregnancy indicated by lethal congenital anomalies based on ethical and medicolegal consideration. The method used to resolve this ethical dilemma is based on clinical ethics theory with systematic consideration of medical indications, patient preferences, quality of life, and contextual features. Medicolegal considerations were also take-into account based on Indonesian Law number 36 of 2009.
 CASE REPORT: This case report shows the termination of pregnancy in a 26-year-old patient with 25–26 weeks’ gestational age. Unfortunately, the patients are referred too late, so because of limited facilities in the rural area, the presence of congenital abnormalities in the fetus is not detected early in the 1st week of pregnancy. The results of obstetric ultrasonography showed multiple congenital anomalies. The ethical dilemma faced by obstetricians is whether to terminate the pregnancy now or after the fetus has reached term gestational age?
 RESULTS: The results of ethical and medicolegal considerations in this case were carried out comprehensively by producing a joint decision between the team of doctors, the patient, and her families. The decision was made after providing adequate information regarding medical indications while taking into account the patient’s viewpoint (patient preference), quality of life, and also contextual features.
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Tu, Yuanpeng, Yuxi Li, Boshen Zhang, et al. "Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21637–45. http://dx.doi.org/10.1609/aaai.v38i19.30162.
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Robust autonomous driving requires agents to accurately identify unexpected areas (anomalies) in urban scenes. To this end, some critical issues remain open: how to design advisable metric to measure anomalies, and how to properly generate training samples of anomaly data? Classical effort in anomaly detection usually resorts to pixel-wise uncertainty or sample synthesis, which ignores the contextual information and sometimes requires auxiliary data with fine-grained annotations. On the contrary, in this paper, we exploit the strong context-dependent nature of segmentation task and design an energy-guided self-supervised frameworks for anomaly segmentation, which optimizes an anomaly head by maximizing likelihood of self-generated anomaly pixels. For this purpose, we design two estimators to model anomaly likelihood, one is a task-agnostic binary estimator and the other depicts the likelihood as residual of task-oriented joint energy. Based on proposed estimators, we devise an adaptive self-supervised training framework, which exploits the contextual reliance and estimated likelihood to refine mask annotations in anomaly areas. We conduct extensive experiments on challenging Fishyscapes and Road Anomaly benchmarks, demonstrating that without any auxiliary data or synthetic models, our method can still achieves comparable performance to supervised competitors. Code is available at https://github.com/yuanpengtu/SLEEG.
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Serrano-Guerrero, Xavier, Guillermo Escrivá-Escrivá, Santiago Luna-Romero, and Jean-Michel Clairand. "A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles." Energies 13, no. 5 (2020): 1046. http://dx.doi.org/10.3390/en13051046.
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Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the results. Through this study, the conditions that electricity demand data must meet for making the time-series analysis useful are established. Finally, one year of real electricity consumption is analyzed for two different cases to evaluate the effect of time-series treatment in the detection of anomalies. The proposed method differentiates between periods of high or low energy demand, identifying contextual anomalies. The results indicate that it is possible to reduce time and effort involved in data analysis, and improve the reliability of monitoring systems, without adding complex procedures.
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Moraes, Anaelena Bragança de, Roselaine Ruviaro Zanini, João Riboldi, and Elsa Regina Justo Giugliani. "Risk factors for low birth weight in Rio Grande do Sul State, Brazil: classical and multilevel analysis." Cadernos de Saúde Pública 28, no. 12 (2012): 2293–305. http://dx.doi.org/10.1590/s0102-311x2012001400008.
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The objective of this study was to identify risk factors for low birth weight in singleton live born infants in Rio Grande do Sul State, Brazil, in 2003, based on data from the Information System on Live Births. The study used both classical multivariate and multilevel logistic regression. Risk factors were evaluated at two levels: individual (live births) and contextual (micro-regions). At the individual level the two models showed a significant association between low birth weight and prematurity, number of prenatal visits, congenital anomalies, place of delivery, parity, sex, maternal age, maternal occupation, marital status, schooling, and type of delivery. In the multilevel models, the greater the urbanization of the micro-region, the higher the risk of low birth weight, while in less urbanized micro-regions, single mothers had an increased risk of low birth considering all live births. Low birth weight varied according to micro-region and was associated with individual and contextual characteristics. Although most of the variation in low birth weight occurred at the individual level, the multilevel model identified an important risk factor in the contextual level.
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Koroliova, Valeria, and Iryna Popova. "Destruction of Communicative Pragmatics in Contemporary Absurdist Dramaturgic Texts." PSYCHOLINGUISTICS 27, no. 2 (2020): 195–212. http://dx.doi.org/10.31470/2309-1797-2020-27-2-195-212.
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The aim of the article is characteristics of mechanisms of pragmatics distraction in communication of active participants of modern Ukrainian plays with features of the theatre of the absurdity. Structural and contextual mechanisms of dialogic speech depragmatization are singled out on factual material. In a dramatic dialogue absurdity is explained as a purposeful instruction to convey the thought about illogicalness and chaotic nature of reality, the aimlessness of a human being. The main methods of the study are descriptive, context-interpreting and presuppositional.
 Study results. One of absurdity occurrence mechanisms is depragmatization – purposeful non-normative usage of language pragmatic resources. We identify structural and contextual violations within depragmatization. Structural violations are characteristic for an absurdist drama in which characters’ cues do not have illocutionary and thematic coherence. Another type of structural violations is conscious violations of formal structure of linguistic units. Role exchange, during which an active participant takes over someone else’s communicative role, is an example of contextual depragmatization. Within contextual violations we also identify the group of cognitive violations which is based on non-observance of causally consecutive and logical connections. Anomalies based on an arbitrary choice of language stylistic means, which are uncoordinated with general principles of stylistic formalization of the text, are considered the contextual variety of depragmatization.
 Conclusions. Structural and contextual communicative violations are used by playwrights to activate the sense of the situational absurdity depicted in a work. Active participants of drama of the absurdity communicate without communicative purpose and taking into account situational needs, which results in actualization of pragmatic potential of used linguistic units, falsification of meaningful speech.
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Kisters, Philipp, Hanno Schreiber, and Janick Edinger. "Categorization of crowd-sensing streaming data for contextual characteristic detection." Journal of Smart Cities and Society 2, no. 2 (2023): 55–75. http://dx.doi.org/10.3233/scs-230013.
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The growing reliance on large wireless sensor networks, potentially consisting of hundreds of nodes, to monitor real-world phenomena inevitably results in large, complex datasets that become increasingly difficult to process using traditional methods. The inadvertent inclusion of anomalies in the dataset, resulting from the inherent characteristics of these networks, makes it difficult to isolate interesting events from erroneous measurements. Simultaneously, improvements in data science methods, as well as increased accessibility to powerful computers, lead to these techniques becoming more applicable to everyday data mining problems. In addition to being able to process large amounts of complex streaming data, a wide array of specialized data science methods enables complex analysis not possible using traditional techniques. Using real-world streaming data gathered by a temperature sensor network consisting of approximately 600 nodes, various data science methods were analyzed for their ability to exploit implicit dependencies embedded in unlabelled data to solve the complex task to identify contextual characteristics. The methods identified during this analysis were included in the construction of a software pipeline. The constructed pipeline reduced the identification of characteristics in the dataset to a trivial task, the application of which led to the detection of various characteristics describing the context in which sensors are deployed.
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Mouton, Alice. "Nommer les dieux hittites : au sujet de quelques épithètes divines." Archiv für Religionsgeschichte 21-22, no. 1 (2020): 225–43. http://dx.doi.org/10.1515/arege-2020-0012.
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AbstractAfter a short overview of Hittite divine epithets (Anatolia of the second half of the second millennium BCE), this paper explores the attestations of two particular divine names, namely “the bloody god U.GUR” and “the vengeful nakkiu-/nakkiwa‐s.” These entities are studied in context in order to determine their identity and functions. Through this contextual analysis, it appears that these supernatural entities are held responsible for various anomalies in the context of Luwian rituals probably coming from the Lower Land (south-central Anatolia).
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Montgomery, Jacob M., Santiago Olivella, Joshua D. Potter, and Brian F. Crisp. "An Informed Forensics Approach to Detecting Vote Irregularities." Political Analysis 23, no. 4 (2015): 488–505. http://dx.doi.org/10.1093/pan/mpv023.
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Electoral forensics involves examining election results for anomalies to efficiently identify patterns indicative of electoral irregularities. However, there is disagreement about which, if any, forensics tool is most effective at identifying fraud, and there is no method for integrating multiple tools. Moreover, forensic efforts have failed to systematically take advantage of country-specific details that might aid in diagnosing fraud. We deploy a Bayesian additive regression trees (BART) model–a machine-learning technique–on a large cross-national data set to explore the dense network of potential relationships between various forensic indicators of anomalies and electoral fraud risk factors, on the one hand, and the likelihood of fraud, on the other. This approach allows us to arbitrate between the relative importance of different forensic and contextual features for identifying electoral fraud and results in a diagnostic tool that can be relatively easily implemented in cross-national research.
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Nandikotkur, Achyuth, Issa Traore, and Mohammad Mamun. "SeniorSentry: Correlation and Mutual Information-Based Contextual Anomaly Detection for Aging in Place." Sensors 23, no. 15 (2023): 6752. http://dx.doi.org/10.3390/s23156752.
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With the ever-growing reliance on IoT-enabled sensors to age in place, a need arises to protect them from malicious actors and detect malfunctions. In an IoT smart home, it is reasonable to hypothesize that sensors near one another can exhibit linear or nonlinear correlations. If substantiated, this property can be beneficial for constructing relationship trends between the sensors and, consequently, detecting attacks or other anomalies by measuring the deviation of their readings against these trends. In this work, we confirm the presence of correlations between co-located sensors by statistically analyzing two public smart-home datasets and a dataset we collected from our experimental setup. Additionally, we leverage the sliding window approach and supervised machine learning to develop a contextual-anomaly-detection model. This model reaches a true positive rate of 89.47% and a false positive rate of 0%. Our work not only substantiates the correlations but also introduces a novel anomaly-detection technique to enhance security in IoT smart homes.
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Reshadi, MohammadHossein, Wen Li, Wenjie Xu, et al. "Deep-Shallow Metaclassifier with Synthetic Minority Oversampling for Anomaly Detection in a Time Series." Algorithms 17, no. 3 (2024): 114. http://dx.doi.org/10.3390/a17030114.
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Anomaly detection in data streams (and particularly time series) is today a vitally important task. Machine learning algorithms are a common design for achieving this goal. In particular, deep learning has, in the last decade, proven to be substantially more accurate than shallow learning in a wide variety of machine learning problems, and deep anomaly detection is very effective for point anomalies. However, deep semi-supervised contextual anomaly detection (in which anomalies within a time series are rare and none at all occur in the algorithm’s training data) is a more difficult problem. Hybrid anomaly detectors (a “normal model” followed by a comparator) are one approach to these problems, but the separate loss functions for the two components can lead to inferior performance. We investigate a novel synthetic-example oversampling technique to harmonize the two components of a hybrid system, thus improving the anomaly detector’s performance. We evaluate our algorithm on two distinct problems: identifying pipeline leaks and patient-ventilator asynchrony.
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Dias, Maurício Araújo, Erivaldo Antônio da Silva, Samara Calçado de Azevedo, Wallace Casaca, Thiago Statella, and Rogério Galante Negri. "An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing." Remote Sensing 12, no. 1 (2019): 43. http://dx.doi.org/10.3390/rs12010043.
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The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings.
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Karadayı, Yıldız, Mehmet N. Aydin, and A. Selçuk Öğrenci. "A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data." Applied Sciences 10, no. 15 (2020): 5191. http://dx.doi.org/10.3390/app10155191.
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Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. It works under the assumption that no prior knowledge about the dataset and anomalies are available. The architecture of the proposed hybrid framework is based on an autoencoder scheme, and it is more efficient in extracting features from the spatio-temporal multivariate datasets compared to the traditional spatio-temporal anomaly detection techniques. We conducted extensive experiments using buoy data of 2005 from National Data Buoy Center and Hurricane Katrina as ground truth. Experiments demonstrate that the proposed model achieves more than 10% improvement in accuracy over the methods used in the comparison where our model jointly processes the spatial and temporal dimensions of the contextual data to extract features for anomaly detection.
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Markov, G. A. "Application of a Neocortex Model to Identify Contextual Anomalies in the Industrial Internet of Things Network Traffic." Automatic Control and Computer Sciences 57, no. 8 (2023): 1018–24. http://dx.doi.org/10.3103/s0146411623080163.
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Hally, Bryan, Luke Wallace, Karin Reinke, and Simon Jones. "A New Spatio-Temporal Selection Method for Estimating Upwelling Medium-Wave Radiation." Remote Sensing 15, no. 14 (2023): 3521. http://dx.doi.org/10.3390/rs15143521.
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Accurate estimates of the unperturbed state of upwelling radiation from the earth’s surface are vital to the detection and classification of anomalous radiation values. Determining radiative anomalies in the landscape is critical for isolating change, a key application being wildfire detection, which is reliant upon knowledge of a location’s radiation budget sans fire. Most techniques for deriving the unperturbed background state of a location use that location’s spatial context, that is, the pixels immediately surrounding the target. Spatial contextual estimation can be subject to error due to occlusion of the pixel’s spatial context and issues such as land cover heterogeneity. This paper proposes a new method of deriving background radiation levels by decoupling the set of prediction pixels used for estimation from the target location in a Spatio-Temporal Selection (STS) process. The process selects training pixels for predictive purposes from a target-centred search area based on their similarity with the target pixel in terms of brightness temperature over a prescribed time period. The proposed STS process was applied to images from the AHI-8 geostationary sensor centred over the Asia-Pacific, and comparisons were made to both brightness temperature estimates from the spatial context and to sensor measurements. This comparison showed that the STS method provided between 10–40% reduction in estimation error over the commonly utilised contextual estimator; in addition, the STS method increased the availability of estimates in comparison to the spatial context by between 12–31%. Image reconstruction using the method resulted in high-fidelity reproductions of the examined landscape, with standing geographic features and areas experiencing thermal anomalies readily identifiable on the resulting images.
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Badri Babadi, Alireza, Ahmad Reza Askari, and Amir Nadri. "Designing a development physical training model for students of Iranian medical sciences universities." Journal of Multidisciplinary Care 11, no. 4 (2022): 196–205. http://dx.doi.org/10.34172/jmdc.2022.1150.
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Background and aims: The importance of developing physical training and the need to expand it among students increases when the social harms and anomalies observed in this space are carefully analyzed. Knowing the pattern and model governing this matter helps prevent social anomalies. Since no study was done to discover this model, the present research was conducted to investigate the design of the development model of physical training for students of Iranian medical sciences universities. Methods: This study was conducted with an exploratory-fundamental nature, a qualitative approach, and the foundation’s data strategy in 2021-2022. The data collection method was a semi-structured interview with 19 specialists and experts using purposeful sampling. The method of coding and forming concepts from the interviews was used to analyze the data. MAXQDATA2020 software was used to analyze the data. Then, the codes were categorized, and a conceptual model was presented. Results: The codes extracted from the interviews included 191 codes in 6 categories of causal conditions, central phenomenon, contextual conditions, intervening conditions, strategic conditions, and consequential conditions, as well as 38 components. Causal conditions include seven components and 27 codes; central phenomenon includes three components and 11 codes; contextual conditions include seven components and 36 codes; intervening conditions include eight components and 41 codes; strategic conditions include eight components and 43 codes and conditions a result includes five components and 24 codes. Conclusion: The developments of physical education and sports for students of medical sciences in Iran, taking into account its causes, axes, contexts, obstacles, and consequences, can inform the managers and planners of sports students of the Ministry of Health about its development process and trend. Students go to physical activity to prevent the wastage of available resources, including financial, human and physical, with careful planning and organization.
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Brett, C. M. C., E. P. Peters, L. C. Johns, P. Tabraham, L. R. Valmaggia, and P. K. Mcguire. "Appraisals of Anomalous Experiences Interview (AANEX): a multidimensional measure of psychological responses to anomalies associated with psychosis." British Journal of Psychiatry 191, S51 (2007): s23—s30. http://dx.doi.org/10.1192/bjp.191.51.s23.
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BackgroundCognitive models of psychosis suggest that whether anomalous experiences lead to clinically relevant psychotic symptoms depends on how they are appraised, the context in which they occur and the individual's emotional responseAimsTo develop and validate a semi-structured interview (the Appraisals of Anomalous Experiences Interview; AANEX) to assess (a) anomalous experiences and (b) appraisal, contextual and response variablesMethodFollowing initial piloting, construct validity was tested via cross-sectional comparison of data from clinical and non-clinical samples with anomalous experiences. Interrater reliability was also assessedResultsScores from AANEX measuring appraisals, responses and social support differentiated the clinical and nonclinical groups. Interrater reliability was satisfactory for 65 of the 71 items. Six items were subsequently amendedConclusionsThe AANEX is avalid multidimensional instrument that provides a detailed assessment of psychotic-like experiences and subjective variables relevant to the development of a need for clinical care
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Thakur, Nirmalya, and Chia Y. Han. "An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments." Information 12, no. 2 (2021): 81. http://dx.doi.org/10.3390/info12020081.
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This framework for human behavior monitoring aims to take a holistic approach to study, track, monitor, and analyze human behavior during activities of daily living (ADLs). The framework consists of two novel functionalities. First, it can perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs to identify a list of distinct behavioral patterns associated with different complex activities. Second, it consists of an intelligent decision-making algorithm that can analyze these behavioral patterns and their relationships with the dynamic contextual and spatial features of the environment to detect any anomalies in user behavior that could constitute an emergency. These functionalities of this interdisciplinary framework were developed by integrating the latest advancements and technologies in human–computer interaction, machine learning, Internet of Things, pattern recognition, and ubiquitous computing. The framework was evaluated on a dataset of ADLs, and the performance accuracies of these two functionalities were found to be 76.71% and 83.87%, respectively. The presented and discussed results uphold the relevance and immense potential of this framework to contribute towards improving the quality of life and assisted living of the aging population in the future of Internet of Things (IoT)-based ubiquitous living environments, e.g., smart homes.
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Zou, Fumin, Yue Xing, Qiang Ren, Feng Guo, Zhaoyi Zhou, and Zihan Ye. "Dynamic Anomaly Detection in Gantry Transactions Using Graph Convolutional Network-Gate Recurrent Unit with Adaptive Attention." Applied Sciences 13, no. 19 (2023): 11068. http://dx.doi.org/10.3390/app131911068.
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With the wide application of Electronic Toll Collection (ETC) systems, the effectiveness of the operation and maintenance of gantry equipment still need to be improved. This paper proposes a dynamic anomaly detection method for gantry transactions, utilizing the contextual attention mechanism and Graph Convolutional Network-Gate Recurrent Unit (GCN-GRU) dynamic anomaly detection method for gantry transactions. In this paper, four different classes of gantry anomalies are defined and modeled, representing gantries as nodes and the connectivity between gantries as edges. First, the spatial distribution of highway ETC gantries is modeled using the GCN model to extract gantry node features. Then, the contextual attention mechanism is utilized to capture the recent patterns of the dynamic transaction graph of the gantries, and the GRU model is used to extract the time-series characteristics of the gantry nodes to dynamically update the gantry leakage. Our model is evaluated on several experimental datasets and compared with other commonly used anomaly detection methods. The experimental results show that our model outperforms other anomaly detection models in terms of accuracy, precision, and other evaluation values of 99%, proving its effectiveness and robustness. This model has a wide application potential in real gantry detection and management.
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Ojebode, Ayokunmi O., and Idowu O. Odebode. "Onomastics, Medicine and Politics in Femi Osofisan’s The Engagement." Theory and Practice in Language Studies 9, no. 5 (2019): 494. http://dx.doi.org/10.17507/tpls.0905.02.
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Onomastics, medicine and politics in this study are a pragmatic way of depicting the psychosocial condition of Nigeria as an underdeveloped nation. The study explores Femi Osofisan’s The Engagement from a literary onomastic standpoint with the aim of exposing socio-political anomalies in Nigeria. Nigerian leaders commit flaws of egoistical and individualistic interests which often go against the consciences of the led. On this premise, the study explores the characters’ names in The Engagement with a view to gaining insight into Nigeria’s sociocultural and political contexts. Furthermore, Postcolonial Theory and Halliday’s Contextual Theory of Meaning serve as the study’s theoretical constructs. The study is predicated on the underdevelopment of Nigeria which is epitomised as a psychological behaviour of characters in a nation that is under the siege of political anarchy and different social vices.
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Imashev, S. A., and S. V. Parov. "Modified Seasonal Decomposition Variations of Earth Magnetic Field Induction Module." Informacionnye Tehnologii 30, no. 2 (2024): 59–67. http://dx.doi.org/10.17587/it.30.59-67.
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In this paper, we present a modification of the classic method of seasonal decomposition of the time series, in particular its application for the analysis of geomagnetic data. Seasonal decomposition is a powerful tool for time series analysis, but its classic implementation does not always provide accurate results when the time series contains amplitude outliers and prolonged gaps. We propose a modified approach to solve this task of seasonal decomposition, by applying an average daily profile. This ensures the extraction of various anomalies in the residual component of the decomposition, in particular, global and contextual outliers, as well as disturbances due to magnetic storms in the variations of geomagnetic field induction module. Keywords: geomagnetic field, seasonal decomposition, data gaps, autocorrelation function, residual component, outliers, magnetic storm, DST index
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Sufi, Fahim. "A New Social Media-Driven Cyber Threat Intelligence." Electronics 12, no. 5 (2023): 1242. http://dx.doi.org/10.3390/electronics12051242.
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Cyber threats are projected to cause USD 10.5 trillion in damage to the global economy in 2025. Comprehending the level of threat is core to adjusting cyber posture at the personal, organizational, and national levels. However, representing the threat level with a single score is a daunting task if the scores are generated from big and complex data sources such as social media. This paper harnesses the modern technological advancements in artificial intelligence (AI) and natural language processing (NLP) to comprehend the contextual information of social media posts related to cyber-attacks and electronic warfare. Then, using keyword-based index generation techniques, a single index is generated at the country level. Utilizing a convolutional neural network (CNN), the innovative process automatically detects any anomalies within the countrywide threat index and explains the root causes. The entire process was validated with live Twitter feeds from 14 October 2022 to 27 December 2022. During these 75 days, AI-based language detection, translation, and sentiment analysis comprehended 15,983 tweets in 47 different languages (while most of the existing works only work in one language). Finally, 75 daily cyber threat indexes with anomalies were generated for China, Australia, Russia, Ukraine, Iran, and India. Using this intelligence, strategic decision makers can adjust their cyber preparedness for mitigating the detrimental damages afflicted by cyber criminals.
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Zhang, Huijie, Ke Ren, Yiming Lin, Dezhan Qu, and Zhenxin Li. "AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data." Sustainability 11, no. 10 (2019): 2944. http://dx.doi.org/10.3390/su11102944.
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Nowadays, huge volume of air quality data provides unprecedented opportunities for analyzing pollution. However, due to the high complexity, most traditional analytical methods focus on abstracting data, so these techniques discard the original structure and limit the understanding of the results. Visual analysis is a powerful technique for exploring unknown patterns since it retains the details of the original data and gives visual feedback to users. In this paper, we focus on air quality data and propose the AirInsight design, an interactive visual analytic system for recognizing, exploring, and summarizing regular patterns, as well as detecting, classifying, and interpreting abnormal cases. Based on the time-varying and multivariate features of air quality data, a dimension reduction method Composite Least Square Projection (CLSP) is proposed, which allows appreciating and interpreting the data patterns in the context of attributes. On the basis of the observed regular patterns, multiple abnormal cases are further detected, including the multivariate anomalies by the proposed Noise Hierarchical Clustering (NHC) method, abruptly changing timestamps by Time diversity (TD) indicator, and cities with unique patterns by the Geographical Surprise (GS) measure. Moreover, we combine TD and GS to group anomalies based on their underlying spatiotemporal correlations. AirInsight includes multiple coordinated views and rich interactive functions to provide contextual information from different aspects and facilitate a comprehensive understanding. In particular, a pair of glyphs are designed that provide a visual representation of the temporal variation in air quality conditions for a user-selected city. Experiments show that CLSP improves the accuracy of Least Square Projection (LSP) and that NHC has the ability to separate noises. Meanwhile, several case studies and task-based user evaluation demonstrate that our system is effective and practical for exploring and interpreting multivariate spatiotemporal patterns and anomalies in air quality data.
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Tian, Yuan, Wendong Wang, and Jingyuan He. "An IIoT Temporal Data Anomaly Detection Method Combining Transformer and Adversarial Training." International Journal of Information Security and Privacy 18, no. 1 (2024): 1–28. http://dx.doi.org/10.4018/ijisp.343306.
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The existing Industrial Internet of Things (IIoT) temporal data analysis methods often suffer from issues such as information loss, difficulty balancing spatial and temporal features, and being affected by training data noise, which can lead to varying degrees of reduced model accuracy. Therefore, a new anomaly detection method was proposed, which integrated Transformer and adversarial training. Firstly, a bidirectional spatiotemporal feature extraction module was constructed by combining Graph Attention Networks (GAT) and Bidirectional Gated Recurrent Unit (BiGRU), which can simultaneously extract spatial and temporal features. Then, by combining multi-scale convolution with Long Short-Term Memory (LSTM), multi-scale contextual information was captured. Finally, an improved Transformer was used to fuse multi-dimensional features, combined with an adversarial-trained variational autoencoder to calculate the anomalies of the input data. This method outperforms other comparison models by conducting experiments on four publicly available datasets.
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Al-Gabalawy, Mostafa. "Removal notice to: “Detecting Anomalies within Unmanned Aerial Vehicle (UAV) Video Based on Contextual Saliency” [Appl. Soft Comput. 96 (2020) 106715]." Applied Soft Computing 110 (October 2021): 107833. http://dx.doi.org/10.1016/j.asoc.2021.107833.
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Albattah, Albatul, and Murad A. Rassam. "A Correlation-Based Anomaly Detection Model for Wireless Body Area Networks Using Convolutional Long Short-Term Memory Neural Network." Sensors 22, no. 5 (2022): 1951. http://dx.doi.org/10.3390/s22051951.
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As the Internet of Healthcare Things (IoHT) concept emerges today, Wireless Body Area Networks (WBAN) constitute one of the most prominent technologies for improving healthcare services. WBANs are made up of tiny devices that can effectively enhance patient quality of life by collecting and monitoring physiological data and sending it to healthcare givers to assess the criticality of a patient and act accordingly. The collected data must be reliable and correct, and represent the real context to facilitate right and prompt decisions by healthcare personnel. Anomaly detection becomes a field of interest to ensure the reliability of collected data by detecting malicious data patterns that result due to various reasons such as sensor faults, error readings and possible malicious activities. Various anomaly detection solutions have been proposed for WBAN. However, existing detection approaches, which are mostly based on statistical and machine learning techniques, become ineffective in dealing with big data streams and novel context anomalous patterns in WBAN. Therefore, this paper proposed a model that employs the correlations that exist in the different physiological data attributes with the ability of the hybrid Convolutional Long Short-Term Memory (ConvLSTM) techniques to detect both simple point anomalies as well as contextual anomalies in the big data stream of WBAN. Experimental evaluations revealed that an average of 98% of F1-measure and 99% accuracy were reported by the proposed model on different subjects of the datasets compared to 64% achieved by both CNN and LSTM separately.
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Qarout, Yazan, Yordan P. Raykov, and Max A. Little. "Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities." Engineering Proceedings 6, no. 1 (2021): 35. http://dx.doi.org/10.3390/i3s2021dresden-10099.
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The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport, and commerce. A large part of monitoring capabilities have already been deployed; however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a low-cost sensor alternative which enables deep understanding of population behaviour, such as the Global Positioning System (GPS) data. However, the automated analysis of such low-dimensional sensor data requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day, or the difference between weekend/weekday trends. We propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data’s contextual realisations, all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AI-HMM), is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies.
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Qarout, Yazan, Yordan P. Raykov, and Max A. Little. "Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities." Sensors 20, no. 3 (2020): 784. http://dx.doi.org/10.3390/s20030784.
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The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport and commerce. A large part of monitoring capabilities have already been deployed; however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a low-cost sensor alternative, which enables deep understanding of population behaviour such as the Global Positioning System (GPS) data. However, the automated analysis of such low dimensional sensor data, requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day or the difference between weekend/weekday trends. In this paper, we propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data’s contextual realisations all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AI-HMM) is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies.
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Chaudhary, Richa, Shruti Jain, Keerthi Maroju, and Anjali Malik. "PANCREAS DISEASE DETECTION AND SEGMENTATION USING ABDOMINAL CT SCAN." International Journal of Advanced Research 11, no. 04 (2023): 1528–36. http://dx.doi.org/10.21474/ijar01/16819.
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Anomalies in the pancreas regional morphology and texture may now be examined by accurately segmenting the organs head, body, and tail on CT images. Hand-drawn pancreatic subregion mapping is labor-intensive, slow, and prone to mistakes. For the zonal segmentation of various anatomical properties, many deep learning networks have been utilized in the present approaches. The three subregions are almost ever visible together in the two-dimensional CT abdominal slices, which limits how the contextual data may be used by the current methods. In this study, we offer a multistage method that uses CT images of pancreatic subregions to accurately and automatically segment 3D objects. The U-Net model is then used to calculate the combined probability of the two maps to perform the best sub regional segmentation. The datasets D1 and D2 of contrast-enhanced abdominal CT images were used to assess the models performance together with a healthy pancreas from the public NIH dataset.
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Junwei Zhang, Xiaofang Chen, Jinkai Sun, Yulu Ren,. "An anomaly Detection Method for Electricity Consumption Data Based on CNN-BiLSTM-Attention." Journal of Electrical Systems 20, no. 2 (2024): 1924–32. http://dx.doi.org/10.52783/jes.1639.
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As the complexity and uncertainty of smart distribution networks increase, data security issues in smart meters have become a pressing challenge, such as false data injection and electricity theft. To ensure fairness, safety, and overall economic efficiency of distribution networks, it is essential to accurately detect abnormal electricity consumption. However, traditional methods relying on on-site inspections by grid personnel suffer from low efficiency and high costs in detecting user anomalies. This paper proposes an electricity consumption data anomaly detection method based on CNN-BiLSTM-Attention. CNN is utilized to extract data features, while BiLSTM and attention mechanisms capture contextual information in sequence data. Furthermore, experiments conducted on data extracted from smart meters demonstrate that the proposed model outperforms other models in anomaly detection, with accuracy, recall, and F1-Score all exceeding 91%. These results validate the effectiveness and feasibility of the proposed method, providing an efficient solution for user anomaly detection in national power grids.
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Knights, Jonathan, Zahra Heidary, and Jeffrey M. Cochran. "Detection of Behavioral Anomalies in Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With a Digital Medicine System." JMIR Mental Health 7, no. 9 (2020): e21378. http://dx.doi.org/10.2196/21378.
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Background Adherence to medication is often represented in the form of a success percentage over a period of time. Although noticeable changes to aggregate adherence levels may be indicative of unstable medication behavior, a lack of noticeable changes in aggregate levels over time does not necessarily indicate stability. The ability to detect developing changes in medication-taking behavior under such conditions in real time would allow patients and care teams to make more timely and informed decisions. Objective This study aims to develop a method capable of identifying shifts in behavioral (medication) patterns at the individual level and subsequently assess the presence of such shifts in retrospective clinical trial data from patients with serious mental illness. Methods We defined the term adherence volatility as “the degree to which medication ingestion behavior fits expected behavior based on historically observed data” and defined a contextual anomaly system around this concept, leveraging the empirical entropy rate of a stochastic process as the basis for formulating anomaly detection. For the presented methodology, each patient’s evolving behavior is used to dynamically construct the expectation bounds for each future interval, eliminating the need to rely on model training or a static reference sequence. Results Simulations demonstrated that the presented methodology identifies anomalous behavior patterns even when aggregate adherence levels remain constant and highlight the temporal dependence inherent in these anomalies. Although a given sequence of events may present as anomalous during one period, that sequence should subsequently contribute to future expectations and may not be considered anomalous at a later period—this feature was demonstrated in retrospective clinical trial data. In the same clinical trial data, anomalous behavioral shifts were identified at both high- and low-adherence levels and were spread across the whole treatment regimen, with 77.1% (81/105) of the population demonstrating at least one behavioral anomaly at some point in their treatment. Conclusions Digital medicine systems offer new opportunities to inform treatment decisions and provide complementary information about medication adherence. This paper introduces the concept of adherence volatility and develops a new type of contextual anomaly detection, which does not require an a priori definition of normal and allows expectations to evolve with shifting behavior, removing the need to rely on training data or static reference sequences. Retrospective analysis from clinical trial data highlights that such an approach could provide new opportunities to meaningfully engage patients about potential shifts in their ingestion behavior; however, this framework is not intended to replace clinical judgment, rather to highlight elements of data that warrant attention. The evidence provided here identifies new areas for research and seems to justify additional explorations in this area.
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Verhoeven, W. M. A., J. I. M. Egger, and N. de Leeuw. "Differentiated psychopharmacological treatment in three genetic subtypes of 22q11.2 deletion syndrome." European Psychiatry 41, S1 (2017): S388—S389. http://dx.doi.org/10.1016/j.eurpsy.2017.02.433.
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IntroductionThe 22q11.2 deletion syndrome (22q11DS), mostly caused by the common deletion including the TBX- and COMT-genes (LCR22A-D), is highly associated with somatic anomalies. The distal deletion (distal of LCR22D) comprises the MAPK1-gene and is associated with specific heart defects. The rare central deletion (LCR22B-D) encompasses the CRKL-gene and shows predominantly urogenital anomalies. 22q11DS also differs in its neuropsychiatric profile: common deletion accompanied by schizophrenia-like psychoses and autism spectrum disorders, distal deletion by anxiety disorders, and central deletion by autistic-like behaviours.ObjectivesInvestigating genetic subtypes of 22q11DS.AimsAchieving a targeted pharmacological treatment based on genetic sub-typing.MethodsThirty-two patients with genetically proven 22q11DS, referred for detailed neuropsychiatric analysis.ResultsApart from two patients with distal deletion and one with central deletion, common 22q11.2 deletion was detected in 29 patients. Those with the common deletion were typified by a history of relapsing schizophrenia-like psychoses and partial non-response to conventional antipsychotics. In most patients, anxieties and mood instability were also manifest. The two patients with a distal deletion predominantly showed anxiety symptoms, while the behaviour of the patient with a central deletion was characterized by symptoms from the autism spectrum. Most patients with a common deletion could successfully be treated with clozapine or quetiapine, often combined with valproic acid. One patient with a distal deletion showed full remission upon treatment with citalopram (the second refused such a pharmacological intervention). The behaviour of the patient with central deletion improved upon contextual measures only.ConclusionsThe genetic subtype of 22q11DS enables targeting of treatment strategy.Disclosure of interestThe authors have not supplied their declaration of competing interest.
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Gomes, Juliana Novo, and Aniela Improta França. "Processing it-cleft sentences in Brazilian Portuguese: an ERP study of leftward-moved constituents in role-reversed sentences." Revista Linguíʃtica 16, Esp. (2020): 495–520. http://dx.doi.org/10.31513/linguistica.2020.v16nesp.a43720.
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In this paper we deepened on the processing of cleft role-reversed structures, based on empirical evidence of standard Brazilian Portuguese (BP). We used the electrophysiological technique (EEG/ERP) to map distinguished syntactic and semantic processes, for instance, the N400 and the P600, addressing the focus structures in It-clefts clauses structured in three different experimental conditions: (i) Congruous Cleft Condition: It was the SURFER that the shark attacked in Hawaii; (ii) Reversed Cleft Condition: It was the SHARK that the surfer attacked in Hawaii; and (iii) Incongruous Cleft Condition: It was the COUCH that the shark attacked in Hawaii. Taken together, our findings suggest that the presence of P600s related to role-reversed sentences in previous studies could be attributed to the syntactic reanalysis, instead of the processing of the role reversed item per se. Also, the presence of an N400 effect to the reversals could be due to the frustration of the strong combination of contextual constraints and strong lexical association. Our results make a unique contribution to the ERP response profiles, specially regarding the relationship between the role-reversals and the animacy violations in the Cleft structural frame. Our ERP findings seem to be compatible with the long-held assumption that the N400 and P600 appear to be modulated by the subject-object asymmetry, and were sensitive to, respectively, the semantic attraction between words in the sentences and, the congruency of the predicate. We thus claim that the syntactic anomalies blocked the detection of semantic anomalies, therefore, semantically incongruous sentences, such as role-reversals were perceived to be odd due to a syntactic constraint satisfaction that assigns the right theta-roles to the verbs arguments despite the semantic cues.-----------------------------------------------------------------------------O PROCESSAMENTO DE SENTENÇAS CLIVADAS NO PORTUGUÊS DO BRASIL: UM ESTUDO DE ERP DE CONSTITUINTES MOVIDOS PARA A ESQUERDA EM SENTENÇAS COM PAPEIS TEMÁTICOS REVERSOSNeste artigo nos profundamos no processamento de estruturas clivadas com argumentos reversos, com base nas evidências empíricas do Português Brasileiro (PB) padrão. Utilizamos a técnica eletrofisiológica (EEG / ERP) para mapear processos sintáticos e semânticos distintos, por exemplo, o N400 e o P600, relacionados às estruturas de foco em cláusulas estruturadas em três diferentes condições experimentais: (i) Condição clivada Congruente: Foi o SURFISTA que o tubarão atacou no Havaí; (ii) Condição de clivada Invertida: Foi o TUBARÃO que o surfista atacou no Havaí; e (iii) Condição clivada incongruente: Foi o SOFÁ que o tubarão atacou no Havaí. Tomados em conjunto, nossos resultados sugerem que a presença de P600s relacionados a sentenças invertidas em estudos anteriores poderia ser atribuída à reanálise sintática, em vez do ser relativo ao processamento do item de papel invertido em si. Além disso, a presença de um efeito N400 nas reversões de argumento pode ser devido à frustração da forte combinação de restrições contextuais combinadas a forte associação lexical. Nossos resultados prestam alguma contribuição para o entendimento dos padrões de resposta do ERP, especialmente no que diz respeito à relação entre as reversões de papéis e as violações de animacidade no quadro estrutural das clivadas. Os ERP que colhemos parecem ser compatíveis com a suposição já de longa data de que o N400 e P600 parecem ser modulados pela assimetria sujeito-objeto, e são também sensíveis à atração semântica entre as palavras nas frases. Assim, afirmamos que as anomalias sintáticas bloquearam a detecção de anomalias semânticas, portanto, sentenças semanticamente incongruentes, como reversões de papéis, foram percebidas como estranhas devido a uma satisfação de restrição sintática que atribui os papéis teta corretos aos argumentos dos verbos, apesar do viés semântica.---Original em inglês.
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Nieuwland, Mante S., and Jos J. A. Van Berkum. "When Peanuts Fall in Love: N400 Evidence for the Power of Discourse." Journal of Cognitive Neuroscience 18, no. 7 (2006): 1098–111. http://dx.doi.org/10.1162/jocn.2006.18.7.1098.
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In linguistic theories of how sentences encode meaning, a distinction is often made between the context-free rule-based combination of lexical-semantic features of the words within a sentence (“semantics”), and the contributions made by wider context (“pragmatics”). In psycholinguistics, this distinction has led to the view that listeners initially compute a local, context-independent meaning of a phrase or sentence before relating it to the wider context. An important aspect of such a two-step perspective on interpretation is that local semantics cannot initially be overruled by global contextual factors. In two spoken-language event-related potential experiments, we tested the viability of this claim by examining whether discourse context can overrule the impact of the core lexical-semantic feature animacy, considered to be an innate organizing principle of cognition. Two-step models of interpretation predict that verb-object animacy violations, as in “The girl comforted the clock,” will always perturb the unfolding interpretation process, regardless of wider context. When presented in isolation, such anomalies indeed elicit a clear N400 effect, a sign of interpretive problems. However, when the anomalies were embedded in a supportive context (e.g., a girl talking to a clock about his depression), this N400 effect disappeared completely. Moreover, given a suitable discourse context (e.g., a story about an amorous peanut), animacy-violating predicates (“the peanut was in love”) were actually processed more easily than canonical predicates (“the peanut was salted”). Our findings reveal that discourse context can immediately overrule local lexical-semantic violations, and therefore suggest that language comprehension does not involve an initially context-free semantic analysis.
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Ghayvat, Hemant, Muhammad Awais, Sharnil Pandya, et al. "Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection." Sensors 19, no. 4 (2019): 766. http://dx.doi.org/10.3390/s19040766.
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Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).
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Malhotra, Rakesh, Terry McNeill, Carrie Francis, and Tim Mulrooney. "Cartographic Presentation as the Central Theme for Geospatial Education." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-237-2019.
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<p><strong>Abstract.</strong> North Carolina Central University is committed to student education and training in cartography and geospatial sciences. This paper demonstrates the importance of applying cartographic principles to train students to convert historical deed records into geospatial data. Students were required to take text information from the 1960s and input this information it into a spatial database. The historical information was recorded on typed deeds in COGO (direction-distance) and the historic coordinate system of 1927 in the 1960s. Students applied cartographic principles that were used to identify contextual and spatial variations and anomalies to flag areas and records that didn’t meet project specifications and to trouble shoot conflicting information.</p><p>This paper demonstrates the usefulness of using cartography as a tool to educate students in allied aspects of geospatial sciences such as creating and managing spatial data. For example, students used tools such as markers and color coding to identify areas of overlap and areas of mismatched records (Figure 1). The authors found that using cartography helped enhance the spatial understanding of the project for students.</p><p>Education is the foundation of projects at North Carolina Central University and cartography has demonstrated appeal at the university level. Various geospatial aspects such as datums and projections, overlays, gaps, overlaps, and converting written information to spatial (geometric) information lend themselves well to cartographic principles. Cartography is an essential element that supports learning and teaching of spatial information as demonstrated by this project. Students were in a better position to understand and detect spatial anomalies with help from cartography than they were without using cartography and relying solely of written information. This enhanced their understanding and use of spatial data.</p>
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Starliper, Nathan, Farrokh Mohammadzadeh, Tanner Songkakul, Michelle Hernandez, Alper Bozkurt, and Edgar Lobaton. "Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses." Sensors 19, no. 3 (2019): 441. http://dx.doi.org/10.3390/s19030441.
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Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices. This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams. We first cluster the data by physical activity using the accelerometer data, and then fit a group lasso model to each activity cluster. We find the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. We show that using activity state-based contextual information increases accuracy while decreasing power usage. We also show that the reduced feature set can be used in other regression models increasing accuracy and decreasing energy burden. We demonstrate the potential reduction in power usage using a custom-designed multi-modal wearable system prototype.