Academic literature on the topic 'Extremist User Detection'
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Journal articles on the topic "Extremist User Detection"
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Минаев, Владимир Александрович, Алена Дмитриевна Реброва, and Александр Викторович Симонов. "DETECTION DESTRUCTIVE CONTENT IN SOCIAL MEDIA BASED ON MACHINE LEARNING MODELS." ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ, no. 1(-) (April 9, 2021): 7–20. http://dx.doi.org/10.36622/vstu.2021.24.1.001.
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В статье обсуждаются модели классификации текстового контента и методы его предварительной обработки с целью выявления деструктивных воздействий в социальных медиа. Показано, что основным источником деструктивного контента выступает профиль пользователей, характеризующийся набором личным данных, содержанием публикаций, параметрами сообщества, аккаунтов сети, сообщений и чатов. Говорится об актуальности автоматизированного сбора и анализа данных с помощью моделей прецедентного и дедуктивного обучения. Рассматриваются их основные разновидности и задачи, решаемые на их основе, включающие прогнозирование и типологизацию в аспекте деструктивного содержания текстов, снижение размерности признаков их описания. Исследованы и применены основные методы векторизации текстов: Bag of Words, TF_IDF, Word2vec. На практических корпусах текстов из социальной сети ВКонтакте решены задачи выявления деструктивного контента, связанного с радикальным исламом. Показано, что с помощью примененных моделей и методов все тексты, включающие деструктивный контент, классифицированы верно. Наиболее высокую точность (0,97) при решении задачи распознавания деструктивного контента дает системная интеграция алгоритма векторизации Bag of Words, метода главных компонент для снижения пространства признаков описания текстов и логистической регрессии или случайного леса как моделей обучения. Сделан вывод, что наборы данных, имеющие связь с исламским радикализмом, характеризуются достаточно четкими признаками, которые хорошо вычисляемы с помощью современных моделей, методов и алгоритмов, и могут эффективно применяться для автоматизированной классификации текстовых массивов с целью выявления их деструктивной направленности. Развитие направления, представленного в статье, связано с увеличением исследуемых корпусов документов, более детальным анализом текстов на основе сложных моделей распознавания латентной экстремистской пропаганды, в том числе - представленной в фото, аудио- и видеоформатах. The article discusses models of classification of text content and methods of its pre-processing in order to identify destructive influences in social media. It is shown that the main source of destructive content is the user profile, which is characterized by a set of personal data, the content of publications, community parameters, network accounts, messages and chats. Automated data collection and analysis using case-based and deductive learning models is discussed. We consider their main varieties and the tasks solved on their basis, including forecasting and typology in the aspect of the destructive content of texts, reducing the dimension of the features of their description. The main methods of text vectorization are investigated and applied: Bag of Words, TF_IDF, Word2vec. The tasks of identifying destructive content related to Islamic radicalism are solved on the practical corpus of texts from the social network VKontakte. It is shown that using the applied models and methods, all texts that include destructive content are classified correctly. The highest accuracy (0.97) in solving the problem of recognizing destructive content is provided by the system integration of the Bag of Words vectorization algorithm, the principal component method for reducing the feature space of text descriptions, and logistic regression or random forest as learning models. It is concluded that the data sets associated with Islamic radicalism are characterized by sufficiently clear features that are well calculated using modern models, methods and algorithms, and can be effectively used for automated classification of text arrays in order to identify their destructive orientation. The development of the direction presented in the article is associated with an increase in the studied corpus of documents, a more detailed analysis of texts based on complex models for recognizing latent extremist propaganda, including those presented in photo, audio and video formats.
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Никитина, L. Nikitina, Коноплева, A. Konopleva, Чудина-Шмидт, and N. Chudina-Shmidt. "The Analysis of Socio-Cultural and Psychological Prerequisites of the Development of Extremism in the Crimean Federal District." Socio-Humanitarian Research and Technology 5, no. 3 (September 19, 2016): 53–60. http://dx.doi.org/10.12737/20938.
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One of the most relevant topics for today´s research is the
problem of the rapid development of extremism. Because of the abstraction
and inaccuracies represented by the law, the fight against
extremist tendencies continues to be difficult. A kind of indicator for
the detection of possible threats from the point of view of radical
ideas is a transitional period. Changes in the geopolitical and socioeconomic
conditions. Lead to the an exacerbation of pre-existing
differences, and thus become catalysts of destructive processes. This
is precisely the situation that exists in the Crimean Federal District
today. The work is devoted to the study of social and psychological
preconditions for the emergence of extremism in the Crimean Federal
District. The data used in the research are obtained as a result of a poll
of the population of the region. The data of the survey has been assessed
in terms of psychological and social factors. The novelty of the research
lies in the implementation of a systematic approach to the problem of
the emergence of extremism and attempts to identify its regional
features.
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Mussiraliyeva, Shynar, Batyrkhan Omarov, Paul Yoo, and Milana Bolatbek. "Applying Machine Learning Techniques for Religious Extremism Detection on Online User Contents." Computers, Materials & Continua 70, no. 1 (2022): 915–34. http://dx.doi.org/10.32604/cmc.2022.019189.
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Kutuzov, Aleksey V. "OPERATIVE-SEARCH MONITORING THE INTERNET AS AN ELEMENT OF COUNTERING CRIMES OF EXTREMIST DIRECTION." Vestnik of Kostroma State University, no. 2 (2020): 235–41. http://dx.doi.org/10.34216/1998-0817-2020-26-2-235-241.
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The article substantiates the need to use Internet monitoring as a priority source of information in countering extremism. Various approaches to understanding the defi nition of the category of «operational search», «law enforcement» monitoring of the Internet are analysed, the theoretical development of the implementation of this category in the science of operational search is investigated. The goals and subjects of law enforcement monitoring are identifi ed. The main attention is paid to the legal basis for the use of Internet monitoring in the detection and investigation of extremist crimes. In the course of the study hermeneutic, formal-logical, logical-legal and comparative-legal methods were employed, which were used both individually and collectively in the analysis of legal norms, achievements of science and practice, and development of proposals to refi ne the conduct of operational-search measures on the Internet when solving extremist crimes. The author’s defi nition of «operational-search monitoring» of the Internet is provided. Proposals have been made to improve the activities of police units when conducting monitoring of the Internet in the context of the search for relevant information to the disclosure and investigation of crimes of that category.
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Zhang, Xuebin, Gabriele Hegerl, Francis W. Zwiers, and Jesse Kenyon. "Avoiding Inhomogeneity in Percentile-Based Indices of Temperature Extremes." Journal of Climate 18, no. 11 (June 1, 2005): 1641–51. http://dx.doi.org/10.1175/jcli3366.1.
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Abstract Using a Monte Carlo simulation, it is demonstrated that percentile-based temperature indices computed for climate change detection and monitoring may contain artificial discontinuities at the beginning and end of the period that is used for calculating the percentiles (base period). This would make these exceedance frequency time series unsuitable for monitoring and detecting climate change. The problem occurs because the threshold calculated in the base period is affected by sampling error. On average, this error leads to overestimated exceedance rates outside the base period. A bootstrap resampling procedure is proposed to estimate exceedance frequencies during the base period. The procedure effectively removes the inhomogeneity.
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van de Weert, Annemarie, and Quirine A. M. Eijkman. "Early detection of extremism? The local security professional on assessment of potential threats posed by youth." Crime, Law and Social Change 73, no. 5 (December 10, 2019): 491–507. http://dx.doi.org/10.1007/s10611-019-09877-y.
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AbstractFrontline professionals such as social workers and civil servants play a crucial role in countering violent extremism. Because of their direct contact with society, firstliners are tasked with detecting individuals that may threaten national security and the democratic rule of law. Preliminary screening takes place during the pre-crime phase. However, without clear evidence or concrete indicators of unlawful action or physical violence, it is challenging to determine when someone poses a threat. There are no set patterns that can be used to identify cognitive radicalization processes that will result in violent extremism. Furthermore, prevention targets ideas and ideologies with no clear framework for assessing terrorism-risk. This article examines how civil servants responsible for public order, security and safety deal with their mandate to engage in early detection, and discusses the side effects that accompany this practice. Based on open-interviews with fifteen local security professionals in the Netherlands, we focus here on the risk assessments made by these professionals. To understand their performance, we used the following two research questions: First, what criteria do local security professionals use to determine whether or not someone forms a potential risk? Second, how do local security professionals substantiate their assessments of the radicalization processes that will develop into violent extremism? We conclude that such initial risk weightings rely strongly on ‘gut feelings’ or intuition. We conclude that this subjectivity may lead to prejudice and/or administrative arbitrariness in relation to preliminary risk assessment of particular youth.
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Lamim Filho, Paulo Cezar Monteiro, Fabiano Bianchini Batista, Robson Pederiva, and Vinicius Augusto Diniz Silva. "Electrical fault diagnosis in induction motors using local extremes analysis." Journal of Quality in Maintenance Engineering 22, no. 3 (August 8, 2016): 321–32. http://dx.doi.org/10.1108/jqme-07-2015-0026.
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Purpose – The purpose of this paper is to introduce an algorithm based only on local extreme analysis of a time sequence to further the detection and diagnosis of inter-turn short circuits and unbalanced voltage supply using vibration signals. Design/methodology/approach – The upper and lower extreme envelopes from a modulated and oscillatory time sequence present a particular characteristic being of, theoretically, symmetrical versions with regard to amplitude reflection around the time axis. Thus, one may say that they carry the same characteristics in terms of waveforms and, consequently, frequency content. These envelopes can easily be built by an interpolation process of the local extremes, maximums and minimums, from the original time sequence. Similar to modulator signals, they contain more detailed and useful information about the required electrical fault frequencies. Findings – Results show the efficiency of the proposed algorithm and its relevance to detecting and diagnosing faults in induction motors with the advantage of being a technique that is easy to implement in any computational code. Practical implications – A laboratory investigation carried out through an experimental setup for the study of faults, mainly related to the stator winding inter-turn short circuit and voltage phase unbalance, is presented. Originality/value – The main contribution of the work is the presentation of an alternative tool to demodulate signals which may be used in real applications like the detection of faults in three-phase induction machines.
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Varga-Fekete, Tímea, Katalin Felvinczi, Emese Kun-Bálint, A. Puskás, E. Nagy, and Z. Brassai. "Accuracy of Ankle-Brachial Index Measurements in Evaluation of Critical Leg Ischemia." Acta Medica Marisiensis 59, no. 2 (April 1, 2013): 85–87. http://dx.doi.org/10.2478/amma-2013-0019.
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Abstract Aims: The ankle-brachial index is an efficient tool for objectively documenting the presence of lower extremity peripheral artery disease. However, its applicability for detection of critical leg ischemia is still controversial. We proposed to determine the diagnostic accuracy of the ankle-brachial index for critical ischemia. Materials and methods: Systolic blood pressure measurements for calculation of the ankle-brachial index were obtained in 90 patients with peripheral artery disease. Ankle-brachial index was computed in 3 different ways (using the lowest ankle pressure, the highest ankle pressure, and the mean of the ankle pressures), sensibility, specificity, positive and negative predictive value and overall accuracy for detecting critical ischemia were determined for each method. A value ≤ 0.4 was taken as cut-off point for critical leg ischemia. Prevalence of coronary and cerebrovascular atherosclerosis and conventional risk factors were also noted. Results: Using the lowest ankle pressure for computing ankle-brachial index provided higher sensitivity, and lower specificity for detecting critical leg ischemia, using the highest pressure was less sensitive, but more specific, and the mean pressure index gave intermediate results. Overall accuracy was highest for the latest method. The prevalence of generalized atherosclerosis was high in peripheral artery disease, but we found no significant difference between the intermittent claudication and the critical ischemia group. Conclusion: Ankle-brachial index measurements, regardless of the method used for calculation, cannot identify or rule out reliably critical leg ischemia. Peripheral artery disease confers an increased risk of cardiovascular disease regardless of symptom status or lower extremity perfusion severity.
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Ilyevsky, V. I. "Recursive Formula for the Random String Word Detection Probability, Overlaps and Probability Extremes." Journal of Mathematics Research 11, no. 2 (March 24, 2019): 171. http://dx.doi.org/10.5539/jmr.v11n2p171.
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In this paper, for the first time ever, the properties of the word detection probability in a random string have been investigated. The formerly known methods led to numerical evaluation of the researched probabilities only. The present work derives the simplest algorithm for calculation of the word’s at least once detection probability in a random string. A recursive formula that considers the overlap capability has been deduced for the probability under study. This formula is being used for the proposition on comparison of the word detection probabilities in a random string for the words with different periods. The result allows determining the structure of words that have maximum and minimum detection probabilities. In particular, words having equal number of alphabetic characters have been studied. It has been established, that for the words in question detection probability is minimal for the ideally symmetrical words that have irreducible period - and maximal for the words devoid of the overlap feature. These results will be useful for molecular genetics, as well as for students studying discrete mathematics, probability theory and molecular biology.
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Celebi, Savas, Ozlem Ozcan Celebi, Serkan Çetin, Elif Hande Ozcan Cetin, Erdem Diker, Sinan Aydogdu, Berkten Berkalp, and Basri Amasyalı. "Invasive screening for lower extremity peripheral artery disease: Killing two birds with one stone?" Vascular 28, no. 2 (October 11, 2019): 196–202. http://dx.doi.org/10.1177/1708538119881592.
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Objectives There is substantial evidence that the majority of cases of lower extremity peripheral artery disease are undetected. As a result, there is great interest in the detection of lower extremity peripheral artery disease through routine screening. However, routine screening of lower extremity peripheral artery disease is still debated. Methods In our cross-sectional study, we included 200 consecutive patients with symptoms suggestive of angina who were undergoing coronary angiography. Irrespective of intermittent claudication, we subsequently performed peripheral angiography to detect lower extremity peripheral artery disease. The predictors of lower extremity peripheral artery disease were analyzed, and the diagnostic utility of these predictors and their combinations were determined. Additionally, the determinants of the amount of radio-opaque material used and peripheral fluoroscopy time were investigated. Results The overall prevalence of lower extremity peripheral disease was 16%. Being older than 65 years, having coronary artery disease and smoking history remained significant predictors after adjusting for other well-known parameters. Having the combination of age ≥65 and smoking was associated with a positive predictive value of 50% (likelihood ratio 5.06), and having all of the predictors was associated with a positive predictive value of 100% (likelihood ratio >1000). Conclusions Routine screening for lower extremity peripheral disease patients undergoing coronary angiography may be useful in selected patients.
Dissertations / Theses on the topic "Extremist User Detection"
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"Data Driven Inference in Populations of Agents." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.53476.
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abstract: In the artificial intelligence literature, three forms of reasoning are commonly employed to understand agent behavior: inductive, deductive, and abductive. More recently, data-driven approaches leveraging ideas such as machine learning, data mining, and social network analysis have gained popularity. While data-driven variants of the aforementioned forms of reasoning have been applied separately, there is little work on how data-driven approaches across all three forms relate and lend themselves to practical applications. Given an agent behavior and the percept sequence, how one can identify a specific outcome such as the likeliest explanation? To address real-world problems, it is vital to understand the different types of reasonings which can lead to better data-driven inference.
This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks.Dissertation/Thesis
Doctoral Dissertation Computer Science 2019
Books on the topic "Extremist User Detection"
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Temperton, David H. Personal monitoring. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199655212.003.0008.
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Personal dose monitoring provides the important check and reassurance that radiation doses to those working with radiation are at a level that complies with dose constraints. The quantity which is relevant to external personal monitoring is the personal dose equivalent Hp(d) defined by the ICRU. Values at different depths d are equated to effective dose or doses to the skin or eye lens. This chapter contains the basic information that the radiation protection practitioner needs on personal monitoring, and the requirements for running a personal dose-monitoring service, including dosimeter calibration, performance testing, and record keeping. Techniques used in different types of dosemeters such as thermoluminescent dosimetry and optically stimulated luminescence, as well as neutron detection are explained, together with practical considerations for eye dose and extremity monitoring. Use of electronic personal dosemeters is discussed and techniques for monitoring internal exposure are described.
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Lee, Christoph I. Decision Rules for Imaging Acute Ankle Injuries. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0031.
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This chapter, found in the bone, joint, and extremity pain section of the book, provides a succinct synopsis of a key study examining the use of the Ottawa ankle rules for imaging acute ankle injuries. This summary outlines the study methodology and design, major results, limitations and criticisms, related studies and additional information, and clinical implications. The study showed that the refined and validated Ottawa Ankle Rules have the potential to reduce approximately 30% to 34% of all foot and ankle radiographs for acute injuries, with 100% sensitivity for reliably detecting foot and ankle fractures. In addition to outlining the most salient features of the study, a clinical vignette and imaging example are included in order to provide relevant clinical context.
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Sampson, Brett G., and Andrew D. Bersten. Therapeutic approach to bronchospasm and asthma. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0111.
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The optimal management of bronchospasm and acute asthma is reliant upon confirmation of the diagnosis of asthma, detection of life-threatening complications, recognition of β2 agonist toxicity, and exclusion of important asthma mimics (such as vocal cord dysfunction and left ventricular failure). β2 agonists, anticholinergics, and corticosteroids are the mainstay of treatment. β2 agonists should be preferentially administered by metered dose inhaler via a spacer, and corticosteroids by the oral route, reserving nebulized (and intravenous) salbutamol, as well as intravenous hydrocortisone, for situations when these routes are not possible. A single intravenous dose of magnesium may be of benefit in severe asthma, but repeat dosing is likely to cause serious side effects. Parenteral administration of adrenaline may prevent the need for intubation in the patient in extremis. Aminophylline has an unfavourable side effect profile and has not been shown to offer additional benefit in adults. However, it does have a role in paediatric asthma. Unproven medical therapies with potential benefit include ketamine, heliox, inhalational anaesthetics, and leukotriene antagonists. The need for ventilatory support is usually preceded by worsening dynamic hyperinflation, exhaustion, hypoxia, reduced conscious state, or a combination of these. While non-invasive ventilation may have a temporizing role to allow time for response to medical therapy, there is insufficient evidence for its use, and should not delay invasive ventilation. If invasive ventilation is indicated, a strategy of hypoventilation and permissive hypercapnoea, minimizes barotrauma and dynamic hyperinflation. Extracorporeal support may have a role as a rescue therapy.
Book chapters on the topic "Extremist User Detection"
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Wei, Yifang, and Lisa Singh. "Detecting Users Who Share Extremist Content on Twitter." In Surveillance in Action, 351–68. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68533-5_17.
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Larbi, Isaac, Clement Nyamekye, Fabien C. C. Hountondji, Gloria C. Okafor, and Peter Rock Ebo Odoom. "Climate Change Impact on Climate Extremes and Adaptation Strategies in the Vea Catchment, Ghana." In African Handbook of Climate Change Adaptation, 1–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-42091-8_95-1.
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AbstractClimate change impact on rainfall and temperature extreme indices in the Vea catchment was analyzed using observation and an ensemble mean of bias-corrected regional climate models datasets for Representative Concentration Pathway (RCP 4.5) scenario. Rainfall extreme indices such as annual total wet-day precipitation (PRCPTOT), extremely wet days (R99P), consecutive wet days (CWD), consecutive dry days (CDD), and temperature indices such as warmest day (TXx) and warmest night (TNx) from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI) were computed for both the historical (1986–2016) and future (2020–2049) period using the RClimdex. The parametric ordinary least square (OLS) regression approach was used to detect trends in the time series of climate change and extreme indices. The results show an increase in mean annual temperature at the rate of 0.02 °C/year and a variability in rainfall at the catchment, under RCP 4.5 scenario. The warmest day and warmest night were projected to increase by 0.8 °C and 0.3 °C, respectively, in the future relative to the historical period. The intensity (e.g., R99p) and frequency (e.g., CDD) of extreme rainfall indices were projected to increase by 29 mm and 26 days, respectively, in the future. This is an indication of the vulnerability of the catchment to the risk of climate disasters (e.g., floods and drought). Adaptation strategies such as early warning systems, availability of climate information, and flood control measures are recommended to reduce the vulnerability of the people to the risk of the projected impact of climate extreme in the future.
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Larbi, Isaac, Clement Nyamekye, Fabien C. C. Hountondji, Gloria C. Okafor, and Peter Rock Ebo Odoom. "Climate Change Impact on Climate Extremes and Adaptation Strategies in the Vea Catchment, Ghana." In African Handbook of Climate Change Adaptation, 1937–53. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_95.
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AbstractClimate change impact on rainfall and temperature extreme indices in the Vea catchment was analyzed using observation and an ensemble mean of bias-corrected regional climate models datasets for Representative Concentration Pathway (RCP 4.5) scenario. Rainfall extreme indices such as annual total wet-day precipitation (PRCPTOT), extremely wet days (R99P), consecutive wet days (CWD), consecutive dry days (CDD), and temperature indices such as warmest day (TXx) and warmest night (TNx) from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI) were computed for both the historical (1986–2016) and future (2020–2049) period using the RClimdex. The parametric ordinary least square (OLS) regression approach was used to detect trends in the time series of climate change and extreme indices. The results show an increase in mean annual temperature at the rate of 0.02 °C/year and a variability in rainfall at the catchment, under RCP 4.5 scenario. The warmest day and warmest night were projected to increase by 0.8 °C and 0.3 °C, respectively, in the future relative to the historical period. The intensity (e.g., R99p) and frequency (e.g., CDD) of extreme rainfall indices were projected to increase by 29 mm and 26 days, respectively, in the future. This is an indication of the vulnerability of the catchment to the risk of climate disasters (e.g., floods and drought). Adaptation strategies such as early warning systems, availability of climate information, and flood control measures are recommended to reduce the vulnerability of the people to the risk of the projected impact of climate extreme in the future.
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Bekelis, Kimon, and Nicos Labropoulos. "Ultrasound in vascular disease —state of the art." In Ultrasound in Clinical Diagnosis. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780199602070.003.0015.
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The current use of ultrasound in vascular disease extends from diagnosis of several disorders to guidance of operative intervention. Duplex ultrasound (DU) is the main diagnostic modality used in patients with carotid disease, deep venous thrombosis (DVT), peripheral arterial disease, monitoring patients with subarachnoid haemorrhage for vasospasm transcranially, and many other routine examinations. The introduction of new technology is permitting the expansion of these applications. New interventions such as carotid stenting and endovascular aneurysm repair have necessitated the use of DU for detecting in-stent restenosis or endoleaks. In preparation for lower extremity bypass, arterial mapping of the lower extremity by DU is used as the sole imaging modality for lower extremity bypass procedures. DU is also used intraoperatively in evaluating carotid endarterectomy, endovenous procedures such as laser and radiofrequency ablation, and the patency of in situ vein bypasses. Most recently, the breakthroughs in bioengineering have resulted in a plethora of applications for vascular ultrasound. Three-dimensional ultrasound has been used in the assessment of carotid plaque volume together with the monitoring of its evolution with time and its response to various treatments. The use of microbubbles as contrast agents during ultrasound imaging allows the assessment of microcirculation and can be used for ultrasound-guided delivery of drugs and genetic therapies. In addition the assessment of brachial flow mediated dilatation and carotid intima–media thickness by DU has provided a non-invasive way of assessing the arterial wall behavior and to evaluate these patients’ risks of adverse cardiovascular events. Finally, the development of intravascular ultrasound has given birth to the field of virtual histology and has opened new frontiers in the measuring intimal thickness and assessing vulnerable plaques. In recent years, the ‘gold standard’ of angiography for arterial mapping of the lower extremities has been challenged by DU. It has been supported as the sole method for evaluating patients undergoing lower extremity interventions by a number of authors. However, scanning the whole arterial tree is time-consuming, and might be obscured in obese patients or patients with tortuous or heavily calcified vessels.
Conference papers on the topic "Extremist User Detection"
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Salimi Naneh Karan, Farshad, and Subhadeep Chakraborty. "Detecting Behavioral Anomaly in Social Networks Using Symbolic Dynamic Filtering." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9643.
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This paper investigates the use of Symbolic Dynamic Filtering (SDF) algorithms in detecting anomalous behavior trends in social networks. Data is generated from an agent-based discrete choice model, which relies on a Markov Decision Process framework for stochastic simulation of decision-making in a social setting, where choices and decisions by individuals are influenced by social interactions. We show that such collective imitative behavior leads to rapid unstable fluctuations in the society, the fluctuation statistics being a weak function of the number of extremist nodes present in the network as well as the prevailing political climate. In this paper, using a time-trace of global opinions in the said society, we investigate the effectiveness of SDF in estimating the number of extremist nodes in a network, and studying the role of unpopular government policies as an enabler of political instability. Spread of influence and ‘recruiting’ by extremist groups through social networks has become an important political issue in recent years. This study is a step in the direction of building tools to preempt and intervene such efforts.
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Wong, Brian Stephen, and Cheng Guan Tui. "Evaluation of Delaminations in Aluminium Honeycomb Structures Using the Mechanical Impedance Technique." In ASME 1997 Turbo Asia Conference. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/97-aa-069.
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This paper describes an evaluation of the capability of a mechanical impedance instrument, for detecting delamination defects in aluminium honeycomb structures. The resonant frequency was found to decrease as the centre of a defect was approached. The defects have been found to be accurately represented by a model for a vibrating plate, which is rigidly clamped at its edges. It was also possible to use resonant frequency to determine the size of the defects in the specimens used in this paper. An irregularly shaped defect showed that the rate of drop in resonant frequency across an extremity of the defect was affected by the radius at the extremity and the proximity to the main central area of the defect. An important result was that an ellipsoidal shaped defect would be sized as a circular defect of diameter equal to the minor diameter of the ellipse. Also a boron skinned honeycomb was found to behave similarly to a glass fibre skinned honeycomb.
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Fischer, Kenneth J., Wen Liu, Clayton Wauneka, Ali Bani-Ahmed, and Patricia Kluding. "Stroke Rehabilitation Enhancement With DVD-Guided Visualization." In ASME 2013 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/sbc2013-14090.
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Stroke is a disorder that affects over 700,000 of people each year, with nearly 500,000 will be left with some sort of long-term disability. While the underlying cause is neurological disability in the region of the brain where the stroke occurred, the functional problem is most often physical disability. Many stroke patients can no longer use their extremities. Lower extremity deficit causes problems with walking and balance, while upper extremity deficit represents significant challenges with common activities of daily living (1). Stoke is considered one of the leading causes of disability in adults (2)! It can also have a tremendous impact on activities of daily living, and may result in dependence on a caregiver. Treatment for stroke has improved, and early detection and treatment can often mitigate the effects and prevent the sequelae of a stroke. Still nearly half a million stroke patients experience some level of physical dysfunction each year. Clearly physical dysfunction due to stroke is an enormous medical problem, and research to improve prevention, treatment and rehabilitation related to stroke is very important.
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Fasel, Timothy R., Hoon Sohn, Gyuhae Park, and Charles R. Farrar. "Application of Frequency Domain ARX Models and Extreme Value Statistics to Impedance-Based Damage Detection." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-43178.
Full textAbstract:
In this paper, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is established. Damage sensitive features that explicitly consider nonlinear system input/output relationships are extracted from the ARX model. Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier. EVS provides superior performance to standard statistical methods because the data of interest are in the tails (extremes) of the damage sensitive feature distribution. The suitability of the ARX model, combined with EVS, to nonlinear damage detection is demonstrated with an impedance-based method that uses piezoelectric (PZT) material as both actuators and sensors. The analyzed data is obtained from a laboratory experiment of a three-story building model.
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Wang, Aaron S., David H. Liang, and Charles A. Taylor. "Aiding Vascular Trauma Detection in the Human Upper Extremities With Image-Based Models of Blood Flow." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192908.
Full textAbstract:
In the hands of an experienced sonographer, a portable diagnostic ultrasound (US) allows for a quick and inexpensive diagnosis of vascular trauma in the field. However, trained sonographers are usually not available at trauma settings, such as in the battlefield. Recent studies by Luo 2007 [1] quantified ultrasound signatures of bleeding at the sites of vessel puncture, which can be used in automated algorithms for ultrasound diagnosis of suspected bleeding sites. However, trauma patients often have large areas of injury requiring extensive scanning to find the bleeding site. An algorithm that can first assess a vascular tree and localize suspicious branches for further examination or therapy would be useful. This study was focused on the upper extremity vascular tree. Specific goals of this study were to 1) demonstrate a physics-based model to characterize normal blood flow, and 2) evaluate its ability to detect vascular abnormalities by looking for flows that deviate from the model.
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Noble, Lilia, Hugh Rees, Pradyumn Thiruvenkatanathan, and Tommy Langnes. "Well Integrity Diagnostics Using Acoustic Event Classification on Distributed Acoustic Sensing Data." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/30930-ms.
Full textAbstract:
Abstract Injection wells experience extremes of pressure and temperature as well as expansion and contraction during their normal operating cycles. This can cause numerous well integrity issues related to corrosion, leakage, degradation of barrier elements, operational practices that all put the health of the well at risk and require appropriate management. This paper will describe a case of a North Sea injection well that over time had developed sustained casing pressure (SCP) in the B-annulus. As a critical well in the development it was necessary to understand the origin and nature of the SCP as this would set further operational plans for the well helping to decide whether the well could continue to be operated safely, would require an intervention, or potentially will be abandoned. A leak investigation was needed to try to determine the source of the pressure, the type of fluid causing the pressure, the fluid path, and whether there was an injection out-of-zone as a result. To address all of the set objectives Distributed Fibre Optic (DFO) system was selected as a technology of choice. DFO provides an advantage over traditional leak detection methods through the ability to simultaneously monitor entire length of the well recording both acoustic and temperature profiles. Distributed Acoustic Sensing (DAS) was used to record acoustic signature of the well helping to determine the leak origin and likely pathway, while Distributed Temperature Sensing (DTS) was used to record well outflow profile and advise on possibility of out-of-zone injection. Use of pattern recognition techniques allowed to extract leak signature from background noise and other acoustic signals helping to pinpoint leak location. As a result of the application of DFO technology coupled with appropriate processing techniques way ahead for the well was identified providing an operator with a confident answer and saving on further intervention costs.
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Tse, Peter W., and Wei Guo. "An Ensemble Empirical Mode Decomposition-Based Lossy Signal Compression Method for a Remote and Wireless Bearing Condition Monitoring System." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70495.
Full textAbstract:
Rolling bearings are one of the most widely used and most likely to fail components in the vast majority of rotating machines. A remote and wireless bearing condition system allows the bearings to be inspected in remote or hazardous environments and increases the machine reliability. To minimize the transmission loads of enormous vibration data for accurate bearing fault diagnosis, a lossy compression method based on ensemble empirical mode decomposition (EEMD) method was proposed for bearing vibration signals in this paper. The EEMD method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different frequency bands of signal components called intrinsic mode functions (IMFs). After applying the EEMD method to the vibration signal, the impulsive signal component related to the faulty bearing is extracted. The noise and irrelevant signal components that are often embedded in the collected vibration signals were removed. In the bearing signal, the distribution for most of the extremes is around zero. Almost all meaningful extremes related to the defect are concentrated in a small fraction of the samples. Hence, this signal compression provides high compression ratio for the bearing vibration signal. To verify the effectiveness of this method, raw vibration signals were collected from an experimental motor and a real traction motor. The proposed lossy signal compression method was applied to these vibration signals to extract the bearing signals and compress them. A comparison of this compression method with the popular wavelet compression method was also conducted. Wirelessly transmitting these compressed data demonstrates that the proposed signal compression method provides high compression performance for bearing vibration signals. Furthermore, the fault diagnosis using the reconstructed signal indicates that most of the impulses relating to the bearing fault are retained, including their periodicity and amplitudes, which are vital for accurate bearing fault diagnosis. Therefore, the compression of the bearing vibration signal contributes not only on the decreases of the file size and the transmission time, but also on the extraction of faulty bearing features to improve the accuracy in signal analysis. With the help of this method, wireless data communication for the remote and wireless bearing condition monitoring system becomes highly efficient, even in a limited bandwidth environment and maintains accurate bearing fault detection without loss of features and the need of transmitting a large amount of vibration data.
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Pettys-Baker, Robert, Crystal Compton, Sophia Utset-Ward, Marc Tompkins, Brad Holschuh, and Lucy E. Dunne. "Design and Development of Valgus-Sensing Leggings." In 2017 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dmd2017-3526.
Full textAbstract:
Performing exercises, especially cutting and pivoting activities, with poor lower extremity mechanics can lead to severe damage of the knee, such as anterior cruciate ligament (ACL) tears [1]. A common movement pattern observed in at-risk athletes is knee valgus. This term refers to the medial collapse of the knee (when the knees falls inward towards the center of the body). Intervention to prevent knee valgus could reduce the chance of injury for at-risk athletes, or re-injury for those recovering from a knee injury. Currently, in patients with knee injuries, knee valgus is monitored by physical therapists, who observe a patient’s movements visually during exercise. The therapists instruct patients on how to identify valgus and how they might correct it. Visual diagnosis of valgus can be difficult and subjective, thereby allowing the unavoidable presence of human error. In addition, monitoring in real time is only possible when the patient is with a therapist. Several studies have focused on the issue of accurate detection of knee valgus by using a variety of systems such as 2D and 3D motion capture systems to track knee and hip movements, dynamometers, and electromyography [2][3][4]. Although these systems are able to determine knee valgus, they are difficult to use, require expensive equipment, and do not provide real-time feedback outside of the clinic setting. The purpose of this study was to inform the design of a valgus-sensing legging by exploring sensor placement options to maximize the magnitude of the sensor response difference between valgus and non-valgus knee bends.
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Panakarajupally, Ragav P., Joseph Elrassi, K. Manigandan, Yogesh P. Singh, and Gregory N. Morscher. "Monitoring Damage in Non-Oxide Composites at High Temperatures Using Carbon-Containing CVD SiC Monofilament Fibers As Embedded Electrical Resistance Sensors." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15937.
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Abstract Electrical resistance has become a technique of interest for monitoring SiC-based ceramic composites. The typical constituents of SiC fiber-reinforced SiC matrix composites, SiC, Si and/or C, are semi-conducive to some degree resulting in the fact that when damage occurs in the form of matrix cracking or fiber breakage, the resistance increases. For aero engine applications, SiC fiber reinforced SiC, sometimes Si-containing, matrix with a BN interphase are often the main constituents. The resistivity of Si and SiC is highly temperature dependent. For high temperature tests, electrical lead attachment must be in a cold region which results in strong temperature effects on baseline measurements of resistance. This can be instructive as to test conditions; however, there is interest in focusing the resistance measurement in the hot section where damage monitoring is desired. The resistivity of C has a milder temperature dependence than that of Si or SiC. In addition, if the C is penetrated by damage, it would result in rapid oxidation of the C, presumably resulting in a change in resistance. One approach considered here is to insert carbon “rods” in the form of CVD SiC monofilaments with a C core to try and better sense change in resistance as it pertains to matrix crack growth in an elevated temperature test condition. The monofilaments were strategically placed in two non-oxide composite systems to understand the sensitivity of ER in damage detection at room temperature as well as elevated temperatures. Two material systems were considered for this study. The first composite system consisted of a Hi-Nicalon woven fibers, a BN interphase and a matrix processed via polymer infiltration and pyrolysis (PIP) which had SCS-6 monofilaments providing the C core. The second composite system was a melt-infiltrated (MI) pre-preg laminate which contained Hi-Nicalon Type S fibers with BN interphases with SCS-Ultra monofilaments providing the C core. The two composite matrix systems represent two extremes in resistance, the PIP matrix being orders of magnitude higher in resistance than the Si-containing pre-preg MI matrix. Single notch tension-tension fatigue tests were performed at 815°C to stimulate crack growth. Acoustic emission (AE) was used along with electrical resistance (ER) to monitor the damage initiation and progression during the test. Post-test microscopy was performed on the fracture specimen to understand the oxidation kinetics and carbon recession length in the monofilaments.
Reports on the topic "Extremist User Detection"
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Scrivens, Ryan, Steven M. Chermak, Joshua D. Freilich, Thomas W. Wojciechowski, and Richard Frank. Detecting Extremists Online: Examining Online Posting Behaviors of Violent and Non-Violent Right-Wing Extremists. RESOLVE Network, September 2021. http://dx.doi.org/10.37805/pn2021.21.remve.
Full textAbstract:
Like most of us, violent extremists often leave a digital footprint behind. Researchers, practitioners, and policymakers raise questions about whether violent individuals can be identified online prior to their attacks offline based on their online posting behaviors. Despite ongoing concerns, few empirically grounded analyses have identified which online users have engaged in violent extremism offline and then assessed their digital footprints, and fewer analyses have identified differences in posting behaviors of those who share extreme ideological beliefs but are violent or non-violent in the offline world. This policy note highlights the importance of both identifying and examining the online behaviors of violent and non-violent extremists in preventing and countering violent extremism (P/CVE) and provides researchers, practitioners, and policymakers with a number of recommendations for detecting and analyzing the online behaviors of violent and non-violent extremists in the future.