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Автор: Grafiati
Опубліковано: 24 серпня 2023

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Статті в журналах з теми "Real word Data"

1

Li, Quanzhi, Sameena Shah, Xiaomo Liu, and Armineh Nourbakhsh. "Data Sets: Word Embeddings Learned from Tweets and General Data." Proceedings of the International AAAI Conference on Web and Social Media 11, no. 1 (May 3, 2017): 428–36. http://dx.doi.org/10.1609/icwsm.v11i1.14859.

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A word embedding is a low-dimensional, dense and real-valued vector representation of a word. Word embeddings have been used in many NLP tasks. They are usually generated from a large text corpus. The embedding of a word captures both its syntactic and semantic aspects. Tweets are short, noisy and have unique lexical and semantic features that are different from other types of text. Therefore, it is necessary to have word embeddings learned specifically from tweets. In this paper, we present ten word embedding data sets. In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets and the general data. The general data consist of news articles, Wikipedia data and other web data. These ten embedding models were learned from about 400 million tweets and 7 billion words from the general data. In this paper, we also present two experiments demonstrating how to use the data sets in some NLP tasks, such as tweet sentiment analysis and tweet topic classification tasks.
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Yamada, Kenta, Hideki Takayasu, and Misako Takayasu. "Estimation of Economic Indicator Announced by Government From Social Big Data." Entropy 20, no. 11 (November 6, 2018): 852. http://dx.doi.org/10.3390/e20110852.

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We introduce a systematic method to estimate an economic indicator from the Japanese government by analyzing big Japanese blog data. Explanatory variables are monthly word frequencies. We adopt 1352 words in the section of economics and industry of the Nikkei thesaurus for each candidate word to illustrate the economic index. From this large volume of words, our method automatically selects the words which have strong correlation with the economic indicator and resolves some difficulties in statistics such as the spurious correlation and overfitting. As a result, our model reasonably illustrates the real economy index. The announcement of an economic index from government usually has a time lag, while our proposed method can be real time.
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3

Tafiadis, Dionysios, Vasiliki Zarokanellou, Alexandra Prentza, Louiza Voniati, and Nafsika Ziavra. "Oral diadochokinetic rates for real words and non-words in Greek-speaking children." Open Linguistics 7, no. 1 (January 1, 2021): 722–38. http://dx.doi.org/10.1515/opli-2020-0178.

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Abstract This study examined the performance of Greek monolingual typically developing (TD) children on diadochokinetic (DDK) rates in real words and non-words and attempted to establish normative data for Greek. The effects of age, type of stimuli and gender were investigated. A total of 380 children aged 4.0–15.0 years as well as a control group of 313 adults participated in the study. Age significantly affected DDK performance, yet normative data differ from other studies. DDK rates for bisyllabic stimuli were faster than DDK rates for trisyllabic stimuli and real words were articulated faster than non-words. Adolescents aged 13.0–15.0 years were slower than adults both in real word and in non-word /ˈpataka/ repetition. Additionally, overall boys were significantly faster than girls. These findings show the need to: (a) implement real word stimuli in DDK tasks in order to better depict an individual’s oral-motor abilities and (b) establish language-specific normative data for TD children.
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4

Maloberti, Alessandro, Andrian Elisa, Leidi Filippo, Monticelli Massimiliano, Galasso Michele, Colombo Valentina, and Giannattasio Cristina. "CARDIOLOGICAL HYPERTENSIVE EMERGENCIES: REAL WORD DATA COMPARED TO GUIDELINES INDICATIONS." Journal of Hypertension 41, Suppl 3 (June 2023): e123. http://dx.doi.org/10.1097/01.hjh.0000940008.49861.5c.

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5

Vulić, Ivan, and Marie-Francine Moens. "Bilingual Distributed Word Representations from Document-Aligned Comparable Data." Journal of Artificial Intelligence Research 55 (April 12, 2016): 953–94. http://dx.doi.org/10.1613/jair.4986.

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We propose a new model for learning bilingual word representations from non-parallel document-aligned data. Following the recent advances in word representation learning, our model learns dense real-valued word vectors, that is, bilingual word embeddings (BWEs). Unlike prior work on inducing BWEs which heavily relied on parallel sentence-aligned corpora and/or readily available translation resources such as dictionaries, the article reveals that BWEs may be learned solely on the basis of document-aligned comparable data without any additional lexical resources nor syntactic information. We present a comparison of our approach with previous state-of-the-art models for learning bilingual word representations from comparable data that rely on the framework of multilingual probabilistic topic modeling (MuPTM), as well as with distributional local context-counting models. We demonstrate the utility of the induced BWEs in two semantic tasks: (1) bilingual lexicon extraction, (2) suggesting word translations in context for polysemous words. Our simple yet effective BWE-based models significantly outperform the MuPTM-based and context-counting representation models from comparable data as well as prior BWE-based models, and acquire the best reported results on both tasks for all three tested language pairs.
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6

Mustikarini, Wening, Risanuri Hidayat, and Agus Bejo. "Real-Time Indonesian Language Speech Recognition with MFCC Algorithms and Python-Based SVM." IJITEE (International Journal of Information Technology and Electrical Engineering) 3, no. 2 (October 29, 2019): 55. http://dx.doi.org/10.22146/ijitee.49426.

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Abstract — Automatic Speech Recognition (ASR) is a technology that uses machines to process and recognize human voice. One way to increase recognition rate is to use a model of language you want to recognize. In this paper, a speech recognition application is introduced to recognize words "atas" (up), "bawah" (down), "kanan" (right), and "kiri" (left). This research used 400 samples of speech data, 75 samples from each word for training data and 25 samples for each word for test data. This speech recognition system was designed using Mel Frequency Cepstral Coefficient (MFCC) as many as 13 coefficients as features and Support Vector Machine (SVM) as identifiers. The system was tested with linear kernels and RBF, various cost values, and three sample sizes (n = 25, 75, 50). The best average accuracy value was obtained from SVM using linear kernels, a cost value of 100 and a data set consisted of 75 samples from each class. During the training phase, the system showed a f1-score (trade-off value between precision and recall) of 80% for the word "atas", 86% for the word "bawah", 81% for the word "kanan", and 100% for the word "kiri". Whereas by using 25 new samples per class for system testing phase, the f1-score was 76% for the "atas" class, 54% for the "bawah" class, 44% for the "kanan" class, and 100% for the "kiri" class.
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7

Whiting, Caroline, Yury Shtyrov, and William Marslen-Wilson. "Real-time Functional Architecture of Visual Word Recognition." Journal of Cognitive Neuroscience 27, no. 2 (February 2015): 246–65. http://dx.doi.org/10.1162/jocn_a_00699.

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Despite a century of research into visual word recognition, basic questions remain unresolved about the functional architecture of the process that maps visual inputs from orthographic analysis onto lexical form and meaning and about the units of analysis in terms of which these processes are conducted. Here we use magnetoencephalography, supported by a masked priming behavioral study, to address these questions using contrasting sets of simple (walk), complex (swimmer), and pseudo-complex (corner) forms. Early analyses of orthographic structure, detectable in bilateral posterior temporal regions within a 150–230 msec time frame, are shown to segment the visual input into linguistic substrings (words and morphemes) that trigger lexical access in left middle temporal locations from 300 msec. These are primarily feedforward processes and are not initially constrained by lexical-level variables. Lexical constraints become significant from 390 msec, in both simple and complex words, with increased processing of pseudowords and pseudo-complex forms. These results, consistent with morpho-orthographic models based on masked priming data, map out the real-time functional architecture of visual word recognition, establishing basic feedforward processing relationships between orthographic form, morphological structure, and lexical meaning.
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8

Maloberti, A., E. Andrian, F. Leidi, M. Massimiliano, M. Galasso, V. Colombo, and C. Giannattasio. "C89 CARDIOLOGICAL HYPERTENSIVE EMERGENCIES: REAL WORD DATA COMPARED TO GUIDELINES INDICATIONS." European Heart Journal Supplements 25, Supplement_D (May 2023): D37. http://dx.doi.org/10.1093/eurheartjsupp/suad111.086.

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Abstract Background The 2018 ESH guidelines have revised the therapeutic goals of cardiological Hypertensive Emergencies (HE) with an indication for a more intensive (target < 140/90 mmHg) and rapid (immediate) Blood Pressure (BP) reduction. Cardiac acute organ damage during HE includes acute myocardial infarction, pulmonary edema, unstable angina pectoris and aortic dissection. However, how much these indications have been applied in clinical practice to date it’s unknown. Aims The first purpose of our study is to analyze the prevalence and clinical characteristics of cardiological HE in our institution. The second purpose is to compare the year before the release of the 2018 guidelines (2017) with the subsequent years (2019) trying to verify adherence to guidelines. Methods This is a single–center retrospective study conducted at the Niguarda Hospital. All patients aged ≥ 18 years with Systolic BP≥ 180 mmHg and/or a Diastolic BP ≥ 120 mmHg with Cardiological Emergency were enrolled. From the Emergency Department (ED) data clinical, anamnestic, blood pressure, symptoms, drug treatment and target achievement were registered. Results Patients with BP > 180/120 mmHg in 2017 were 706 out of a total of 73795 accesses (0.96%) and 601 over 67273 (0.89%) in 2019. 246 (34.84%) in 2017 were HE of which 144 (58.53%) were cardiological: aortic dissection 1 (0.69%), acute coronary syndrome 52 (36.11%), acute pulmonary edema 35 (24.30%), cardiac decompensation 91 (63.19%). During 2019 similar figures were founded with 286 (47.58%) HE of which 286 (47.58%) were cardiological: aortic dissection 2 (1.43%), acute coronary syndrome 43 (30.93%), acute pulmonary edema 20 (14.39%), cardiac decompensation 76 (54.68%). The reduction in BP obtained in ED was significantly greater in 2017 than in 2019 (44.7±31.4 vs 35.4±24.5 mmHg, p = 0.011) with a lower target reaching in 2019 (28.9 vs 51.4%, p<0.001). Pulmonary edema is the cardiological HE on which a greater pressure reduction is obtained and therefore in which the target set by the guidelines is more frequently reached. Conclusions The recommendation for a more intense and rapid BP reduction in cardiological HE seems to be not accepted from ED clinicians that persist to reduce BP accordingly to previous guidelines.
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9

Damholdt, Malene Flensborg, Vestergaard Christina, Anna Kryvous, Catharina Vesterager Smedegaard, and Johanna Seibt. "What is in three words? Exploring a three-word methodology for assessing impressions of a social robot encounter online and in real life." Paladyn, Journal of Behavioral Robotics 10, no. 1 (December 31, 2019): 438–53. http://dx.doi.org/10.1515/pjbr-2019-0034.

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AbstractWe explore the impressions and conceptualisations produced by participants after their first encounter with the teleoperated robot, Telenoid R1.Participants were invited to freely report the first three words that came to mind after seeing the robot. Here we triangulate (i) three-word data from an online survey (n=340) where respondents saw a brief video of the Telenoid with (ii) three-word data from an interaction study where participants interacted with a physically present Telenoid (n=75) and, (iii) data from qualitative interviews (n=7) with participants who had engaged with the Telenoid. Data were subjected to sentiment analysis, linguistic analysis and regression analysis.Ranking of the most frequently produced words in the two groups revealed an overlap on the top-10 produced words (6 out of 10 words). Sentiment analysis and regression revealed an association between negative predicates and the online condition. Sentiments were not convincingly associated with age or gender. Linguistic categorisations of the data revealed that especially adjectives expressing response-dependent features were frequent. We did not find any consistent statistical effect on categorising the words into cognitive and emotional predicates.The proposed three-word method offers, unguided approach to explore initial conceptualisations of robots.
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10

Roberto, Giuseppe, Andrea Spini, Claudia Bartolini, Valentino Moscatelli, Alessandro Barchielli, Davide Paoletti, Silvano Giorgi, et al. "Real word evidence on rituximab utilization: Combining administrative and hospital-pharmacy data." PLOS ONE 15, no. 3 (March 12, 2020): e0229973. http://dx.doi.org/10.1371/journal.pone.0229973.

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Дисертації з теми "Real word Data"

1

Rodittis, Katherine, and Patrick Mattingly. "USING MICROSOFT’S COMPONENT OBJECT MODEL (COM) TO IMPROVE REAL-TIME DISPLAY DEVELOPMENT FOR THE ADVANCED DATA ACQUISITION AND PROCESSING SYSTEM (ADAPS)." International Foundation for Telemetering, 2000. http://hdl.handle.net/10150/606801.

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International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California
Microsoft’s Component Object Model (COM) allows us to rapidly develop display and analysis features for the Advanced Data Acquisition and Processing System (ADAPS).
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2

Anantharajah, Kaneswaran. "Robust face clustering for real-world data." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/89400/1/Kaneswaran_Anantharajah_Thesis.pdf.

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This thesis has investigated how to cluster a large number of faces within a multi-media corpus in the presence of large session variation. Quality metrics are used to select the best faces to represent a sequence of faces; and session variation modelling improves clustering performance in the presence of wide variations across videos. Findings from this thesis contribute to improving the performance of both face verification systems and the fully automated clustering of faces from a large video corpus.
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3

Allen, Brett. "Learning body shape models from real-world data /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/6969.

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Apeh, Edward Tersoo. "Adaptive algorithms for real-world transactional data mining." Thesis, Bournemouth University, 2012. http://eprints.bournemouth.ac.uk/20989/.

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The accurate identification of the right customer to target with the right product at the right time, through the right channel, to satisfy the customer’s evolving needs, is a key performance driver and enhancer for businesses. Data mining is an analytic process designed to explore usually large amounts of data (typically business or market related) in search of consistent patterns and/or systematic relationships between variables for the purpose of generating explanatory/predictive data models from the detected patterns. It provides an effective and established mechanism for accurate identification and classification of customers. Data models derived from the data mining process can aid in effectively recognizing the status and preference of customers - individually and as a group. Such data models can be incorporated into the business market segmentation, customer targeting and channelling decisions with the goal of maximizing the total customer lifetime profit. However, due to costs, privacy and/or data protection reasons, the customer data available for data mining is often restricted to verified and validated data,(in most cases,only the business owned transactional data is available). Transactional data is a valuable resource for generating such data models. Transactional data can be electronically collected and readily made available for data mining in large quantity at minimum extra cost. Transactional data is however, inherently sparse and skewed. These inherent characteristics of transactional data give rise to the poor performance of data models built using customer data based on transactional data. Data models for identifying, describing, and classifying customers, constructed using evolving transactional data thus need to effectively handle the inherent sparseness and skewness of evolving transactional data in order to be efficient and accurate. Using real-world transactional data, this thesis presents the findings and results from the investigation of data mining algorithms for analysing, describing, identifying and classifying customers with evolving needs. In particular, methods for handling the issues of scalability, uncertainty and adaptation whilst mining evolving transactional data are analysed and presented. A novel application of a new framework for integrating transactional data binning and classification techniques is presented alongside an effective prototype selection algorithm for efficient transactional data model building. A new change mining architecture for monitoring, detecting and visualizing the change in customer behaviour using transactional data is proposed and discussed as an effective means for analysing and understanding the change in customer buying behaviour over time. Finally, the challenging problem of discerning between the change in the customer profile (which may necessitate the effective change of the customer’s label) and the change in performance of the model(s) (which may necessitate changing or adapting the model(s)) is introduced and discussed by way of a novel flexible and efficient architecture for classifier model adaptation and customer profiles class relabeling.
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5

Naulleau, Patrick. "Optical signal processing and real world applications /." Online version of thesis, 1993. http://hdl.handle.net/1850/12136.

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6

Vogetseder, Georg. "Functional Analysis of Real World Truck Fuel Consumption Data." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1148.

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This thesis covers the analysis of sparse and irregular fuel consumption data of long

distance haulage articulate trucks. It is shown that this kind of data is hard to analyse with multivariate as well as with functional methods. To be able to analyse the data, Principal Components Analysis through Conditional Expectation (PACE) is used, which enables the use of observations from many trucks to compensate for the sparsity of observations in order to get continuous results. The principal component scores generated by PACE, can then be used to get rough estimates of the trajectories for single trucks as well as to detect outliers. The data centric approach of PACE is very useful to enable functional analysis of sparse and irregular data. Functional analysis is desirable for this data to sidestep feature extraction and enabling a more natural view on the data.

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7

Langdell, Stephen James. "Radial basis function networks for modelling real world data." Thesis, University of Huddersfield, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285590.

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8

Tsapeli, Theofania Kleio. "Understanding real-world phenomena from human-generated sensor data." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8445/.

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Nowadays, there is an increasing data availability. Smartphones, wearable devices, social media, web browsing information and sales recordings are only few of the newly available information sources. Analysing this kind of information is an important step towards understanding human behaviour. In this dissertation, I propose novel techniques for uncovering the complex dependencies between factors extracted from raw sensor data and real-world phenomena and I demonstrate the potential of utilising the vast amount of human digital traces in order to better understand human behaviour and factors influenced by it. In particular, two main problems are considered: 1) whether there is a dependency between social media data and traded assets prices and 2) how smartphone sensor data can be used to understand factors that influence our stress level. In this thesis, I focus on uncovering the structural dependencies among factors of interest rather than on the detection of mere correlation. Special attention is given on enhancing the reliability of the findings by developing techniques that can better handle the specific characteristics of the examined datasets. Although the developed approaches are motivated by specific problems related to human-generated sensor data, they are general and can be applied in any dataset with similar characteristics.
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9

Saunders, L. J. "Studies on real world visual field data in glaucoma." Thesis, City, University of London, 2015. http://openaccess.city.ac.uk/16170/.

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Glaucoma is a leading cause of blindness. As a progressive condition, it is important to monitor how the visual field (VF) changes over time with perimetry in preventing vision from deteriorating to a stage where quality of life is affected. However, there is little evidence of how clinical measurements correlate with meaningful quality of life landmarks for the patient or, by extension, the proportion of patients in danger of progressing to these landmarks. Further, measurement variability associated with visual fields make it difficult to monitor true change over time. The purpose of this thesis was to use large-scale clinical data (almost 500,000 VFs) to address some of these issues. The first study attempted to relate clinical measurements of glaucoma severity to UK legal fitness to drive status. Legal fitness to drive (LFTD) was estimated using the integrated visual field as a surrogate of the Esterman test, which is the approved method by the UK DVLA of defining LFTD, while the mean deviation (MD) was used to represent defect severity. An MD of -14dB or worse in the better eye was found to be associated with a 92% (95% Confidence Interval [CI]: 87-95%) probability of being legally unfit to drive. The second study used a statistical model to estimate the number of patients progressing at rates that could lead to this landmark of significant visual impairment or blindness in their predicted remaining lifetime. A significant minority of patients were progressing at rates that could lead to statutory blindness, as defined by the US Social Security Administration, in their predicted remaining lifetime (5.2% [CI: 4.5-6.0%]) with a further 10% in danger of becoming legally unfit to drive (10.4% [CI: 9.4-11.4%]). More than 90% (CI: 85.7-94.3%) of patients predicted to progress to statutory blindness had an MD worse than -6dB in at least one eye at presentation, suggesting an association between baseline VF damage and risk of future impairment. The next section investigated whether choice of testing algorithm, SITA Standard or SITA Fast, affected the time taken to detect progression in VF follow-up. The precision of the tests was measured using linear modelling techniques and the impact of these differences was analysed using simulations. Though SITA Fast was found to be slightly less precise, no evidence was found to suggest that this resulted in progression being detected later. The final study evaluated a validated and published risk calculator, which utilised baseline risk factors to profile risk of fast progression. A simpler model using baseline VF data was developed to have similar statistical properties for comparison(including equivalent R2 statistics). The results suggested that risk calculators with low R2 statistics had little utility for predicting future progression rate in clinical practice. Together these results contribute a variety of novel findings and demonstrate the benefit of using large quantities of data collected from the everyday clinical milieu to extend clinical knowledge.
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10

Lövenvald, Frans-Lukas. "FINDING ANOMALOUS TIME FRAMES IN REAL-WORLD LOG DATA." Thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163311.

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Anomaly detection is a huge fi€eld of research focused on the task of €finding weird or outlying points in data. Th‘is task is useful in all fi€elds that handle large amounts of data and is therefore a big topic of research. Th‘e focus of research often lies in fi€nding novel approaches for €finding anomalies in already labeled and well-understood data. ‘This thesis will not focus on a novel algorithm but instead display and discuss the power of an anomaly detection process that focuses on feature engineering and feature exploration. Th‘e thesis will also compare two unsupervised anomaly classifi€cation algorithms, namely k-nearest neighbours and principal component analysis, in terms of explainability and scalability. ‘The results concludes that sometimes feature engineering can display anomalies just as well as novel and complex anomaly detection algorithms.
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Книги з теми "Real word Data"

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Abou-Nasr, Mahmoud, Stefan Lessmann, Robert Stahlbock, and Gary M. Weiss, eds. Real World Data Mining Applications. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-07812-0.

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2

Real world print production. Berkeley, Calif: Peachpit Press, 2007.

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3

Ahmad, Muhammad Aurangzeb, Cuihua Shen, Jaideep Srivastava, and Noshir Contractor, eds. Predicting Real World Behaviors from Virtual World Data. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07142-8.

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4

Finlay, Mark. Real-world fractals. New York: M&T Books, 1993.

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5

1980-, McCartan Kieran, ed. Real world research. Hoboken: Wiley, 2016.

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6

Reinfeld, Eric. Real world After Effects. Glen Ellen, Calif: LightSpeed Publishing, 1997.

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7

1950-, Mukhopadhyay Nitis, Datta Sujay, and Chattopadhyay Saibal, eds. Applied sequential methodologies: Real-world examples with data analysis. New York: M. Dekker, 2004.

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8

Stamp, Mark. Applied cryptanalysis: Breaking ciphers in the real world. Hoboken, N.J: John Wiley & Sons, 2007.

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9

Real world print production with Adobe Creative Suite applications. Berkeley, Calif: Peachpit, 2009.

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10

Aboul-Magd, Osama. Real-time networking in a hyperconnected world. Research Triangle Park, NC: Nortel Press, 2008.

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Частини книг з теми "Real word Data"

1

Sridhar, Shailesh, E. Saahitya, Satyam Shivam Sundaram, and Bhaskarjyoti Das. "Tendulkar’s Cat and Schrodinger’s Bat—Knowledge-Enhanced Real-Word Error Correction." In Emerging Technologies in Data Mining and Information Security, 381–95. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9774-9_37.

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2

Pollak, Petr, and Michal Borsky. "Small and Large Vocabulary Speech Recognition of MP3 Data under Real-Word Conditions: Experimental Study." In E-Business and Telecommunications, 409–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35755-8_29.

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3

Tran, Quang Duy, and Fabio Di Troia. "Word Embeddings for Fake Malware Generation." In Silicon Valley Cybersecurity Conference, 22–37. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24049-2_2.

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AbstractSignature and anomaly-based techniques are the fundamental methods to detect malware. However, in recent years this type of threat has advanced to become more complex and sophisticated, making these techniques less effective. For this reason, researchers have resorted to state-of-the-art machine learning techniques to combat the threat of information security. Nevertheless, despite the integration of the machine learning models, there is still a shortage of data in training that prevents these models from performing at their peak. In the past, generative models have been found to be highly effective at generating image-like data that are similar to the actual data distribution. In this paper, we leverage the knowledge of generative modeling on opcode sequences and aim to generate malware samples by taking advantage of the contextualized embeddings from BERT. We obtained promising results when differentiating between real and generated samples. We observe that generated malware has such similar characteristics to actual malware that the classifiers are having difficulty in distinguishing between the two, in which the classifiers falsely identify the generated malware as actual malware almost $$90\%$$ of the time.
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Christen, Peter, Thilina Ranbaduge, and Rainer Schnell. "Real-world Applications." In Linking Sensitive Data, 345–57. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1_13.

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Consoli, Sergio, Domenico Perrotta, and Marco Turchi. "Reduced Variable Neighbourhood Search for the Generation of Controlled Circular Data." In Variable Neighborhood Search, 83–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69625-2_7.

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AbstractA number of artificial intelligence and machine learning problems need to be formulated within a directional space, where classical Euclidean geometry does not apply or needs to be readjusted into the circle. This is typical, for example, in computational linguistics and natural language processing, where language models based on Bag-of-Words, Vector Space, or Word Embedding, are largely used for tasks like document classification, information retrieval and recommendation systems, among others. In these contexts, for assessing document clustering and outliers detection applications, it is often necessary to generate data with directional properties and units that follow some model assumptions and possibly form close groups. In the following we propose a Reduced Variable Neighbourhood Search heuristic which is used to generate high-dimensional data controlled by the desired properties aimed at representing several real-world contexts. The whole problem is formulated as a non-linear continuous optimization problem, and it is shown that the proposed Reduced Variable Neighbourhood Search is able to generate high-dimensional solutions to the problem in short computational time. A comparison with the state-of-the-art local search routine used to address this problem shows the greater efficiency of the approach presented here.
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Chow, Shein-Chung. "Real-World Data and Real-World Evidence." In Innovative Methods for Rare Disease Drug Development, 141–66. Boca Raton, FL : CRC Press, 2021. |: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003049364-8.

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Sun, Xin. "Real-World Data and Real-World Evidence." In Advanced Statistics in Regulatory Critical Clinical Initiatives, 105–32. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003107323-5.

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Gunjal, Baisa L. "Real-World Applications of Data Science." In Data Science, 33–56. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003283249-3.

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Wallis, W. D. "Transmitting Data." In Mathematics in the Real World, 143–56. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8529-2_10.

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Wallis, W. D. "Data: Distributions." In Mathematics in the Real World, 45–63. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8529-2_4.

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Тези доповідей конференцій з теми "Real word Data"

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Yoshitake, Masato, Naoko Nitta, and Noboru Babaguchi. "Real-World Observation Extraction from Microblog Based on Word Associative Relations." In 2016 IEEE Second International Conference on Multimedia Big Data (BigMM). IEEE, 2016. http://dx.doi.org/10.1109/bigmm.2016.75.

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Islam, Aminul, and Diana Inkpen. "Real-word spelling correction using Google web 1Tn-gram data set." In Proceeding of the 18th ACM conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1645953.1646205.

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Uebersax, Dominique, Juergen Gall, Michael Van den Bergh, and Luc Van Gool. "Real-time sign language letter and word recognition from depth data." In 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE, 2011. http://dx.doi.org/10.1109/iccvw.2011.6130267.

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Song, Heekyoung, and Yong-Wook Kim. "Real word data for histologic type of endometrial cancer as molecular classification." In KSGO 2023. Korea: Korean Society of Gynecologic Oncology, 2023. http://dx.doi.org/10.3802/jgo.2023.34.s1.e04.

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Shahidi, Parham, Steve C. Southward, and Mehdi Ahmadian. "Real-Time Estimation of Temporal Word Boundaries Without Linguistic Knowledge." In IEEE/ASME/ASCE 2008 Joint Rail Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/jrc2008-63066.

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A novel real-time algorithm has been developed for estimating temporal word boundaries in measured speech without the need for interpretation of individual words. This algorithm is the foundational building block of a method for estimating a variety of key metrics such as word production rate, phrase production rate, words per phrase, etc., that are indicative of human mental states. In particular, we are interested in developing a system for monitoring locomotive crew alertness. The majority of existing speech processing algorithms relies on pre-recorded speech corpora. The real-time algorithm presented here is unique in that it employs a simple and efficient pattern matching method to identify temporal word boundaries by monitoring threshold crossings in the speech power signal. This algorithm eliminates the need to interpret the speech, and still produces reasonable estimates of word boundaries. The proposed algorithm has been tested with a batch of experimentally recorded speech data and with real time speech data. The results from the testing are outlined in this paper.
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Müller, C., V. Kiver, E. F. Solomayer, G. Wagenpfeil, C. Neeb, J. U. Blohmer, A. V. Abramian, et al. "CDK4/6 inhibitors in advanced HR+/HER2- breast cancer – a multicenter real word data analysis." In 41. Jahrestagung der Deutschen Gesellschaft für Senologie e.V. – Gemeinsam gegen Brustkrebs: optimale Behandlung für jede Patientin. Georg Thieme Verlag, 2022. http://dx.doi.org/10.1055/s-0042-1748411.

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Hota, Shweta, Elmie Cananea, Wendy Martin, Dominique Clement, Bernadette Solis, John Ramage, and Rajaventhan Srirajaskanthan. "P253 First UK real-word data on patients with carcinoid syndrome on long-term telotristat therapy." In Abstracts of the BSG Campus, 21–29 January 2021. BMJ Publishing Group Ltd and British Society of Gastroenterology, 2021. http://dx.doi.org/10.1136/gutjnl-2020-bsgcampus.327.

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Murakami, Riki, and Basabi Chakraborty. "Neural Topic Models for Short Text Using Pretrained Word Embeddings and Its Application To Real Data." In 2021 IEEE 4th International Conference on Knowledge Innovation and Invention (ICKII). IEEE, 2021. http://dx.doi.org/10.1109/ickii51822.2021.9574752.

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Nóbrega, Ivna Lacerda Pereira, Igor Santiago Bessa, Lucas Silvestre Mendes, José Artur Costa D'Almeida, Gabriela Joca Martins, and Milena Sales Pitombeira. "Anti-CD20 therapy in Multiple Sclerosis: real-word data from a single-center in Northeast Brazil." In PROCEEDINGS OF THE BCTRIMS 24TH ANNUAL MEETING. Galoa, 2023. http://dx.doi.org/10.17648/bctrims-2023-167457.

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Yazdizadeh, Tina, and Wei Shi. "Performance Evaluation for the use of ELMo Word Embedding in Cyberbullying Detection." In 3rd International Conference on Data Science and Machine Learning (DSML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121511.

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Communication using modern internet technologies has revolutionized the ways humans exchange information.. Despite the numerous advantages offered by such technology, its applicability is still limited due to problems stemming from personal attacks and pseudoattacks. On social media platforms, these toxic contents may take the form of texts (e.g., online chats, emails), speech, and even images and movie clips. Because the cyberbullying of an individual via the use of such toxic digital content may have severe consequences, it is essential to design and implement, among others, various techniques to automatically detect, using machine learning approaches, cyberbullying on social media. It is important to use word embedding techniques to represent words for text analysis, typically in the form of a real-valued vector that encodes the meaning of words. The extracted embeddings are used to decide if a digital input contains cyberbullying contents. Supplying strong word representations to classification methods is a key facet of such detection approaches. In this paper, we evaluate the ELMo word embedding against three other word embeddings, namely, TF-IDF, Word2Vec, and BERT, using three basic machine learning models and four deep learning models. The results show that the ELMo word embeddings have the best results when combined with neural network-based machine learning models.
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Звіти організацій з теми "Real word Data"

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Gerdes, Mindy, Don Scoffield, and Jonathan Coignard. Task 3: Validation Using Real-World Data. Office of Scientific and Technical Information (OSTI), April 2017. http://dx.doi.org/10.2172/1483807.

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Smoyer-Tomic, Karen, Kate Young, and Christopher Winchester. Identifying real-world data for observational studies: a systematic approach. Oxford PharmaGenesis Ltd., May 2014. http://dx.doi.org/10.21305/ispor2014.001.

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Lipkin, H. J. Quarks, QCD (quantum chromodynamics) and the real world of experimental data. Office of Scientific and Technical Information (OSTI), July 1987. http://dx.doi.org/10.2172/6275793.

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Author, Not Given. Transportation Secure Data Center: Real-World Data for Planning, Modeling, & Analysis (Fact Sheet) (Revised). Office of Scientific and Technical Information (OSTI), March 2012. http://dx.doi.org/10.2172/1016419.

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Lascurain, Mary Beth, Oscar Franzese, Gary J. Capps, Adam Siekmann, Neil Thomas, Tim J. LaClair, Alan M. Barker, and Helmut E. Knee. Medium Truck Duty Cycle Data from Real-World Driving Environments: Final Report. Office of Scientific and Technical Information (OSTI), November 2012. http://dx.doi.org/10.2172/1081995.

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Franzese, Oscar, Mary Beth Lascurain, and Gary J. Capps. Medium Truck Duty Cycle Data from Real-World Driving Environments: Project Interim Report. Office of Scientific and Technical Information (OSTI), January 2011. http://dx.doi.org/10.2172/1081683.

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Powers, Sarah S., and Joshua Lothian. Synthetic graph generation for data-intensive HPC benchmarking: Scalability, analysis and real-world application. Office of Scientific and Technical Information (OSTI), December 2014. http://dx.doi.org/10.2172/1214496.

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Davis, W., R. Grip, M. McKay, R. and Stotler, D. P. Pfaff, and A. P. Post-Zwicker. Teaching Contemporary Physics Topics using Real-Time Data Obtained via the World Wide Web. Office of Scientific and Technical Information (OSTI), December 1998. http://dx.doi.org/10.2172/2385.

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Tian, Nan, Aude Fleurant, Alexandra Kuimova, Pieter Wezeman, and Siemon Wezeman. Trends in World Military Expenditure, 2018. Stockholm International Peace Research Institute, April 2019. http://dx.doi.org/10.55163/ufdk7864.

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World military expenditure is estimated to have been $1822 billion in 2018. It was 2.6 per cent higher in real terms than in 2017 and 5.4 per cent higher than in 2009. Global military spending has been gradually rising following a post-2009 low in 2014. It is now 76 per cent higher than the post-cold war low in 1998. This Fact Sheet presents regional and selected national military expenditure data for 2018 and trends over the decade 2009–18. The data is from the updated SIPRI Military Expenditure Database, which provides military expenditure data by country for the years 1949–2018.
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Tian, Nan, Alexandra Kuimova, Diego Lopes da Silva, Pieter Wezeman, and Siemon Wezeman. Trends in World Military Expenditure, 2019. Stockholm International Peace Research Institute, April 2019. http://dx.doi.org/10.55163/zzcq1349.

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Global military expenditure is estimated to have been $1917 billion in 2019, the highest level since 1988. The total was 3.6 per cent higher in real terms than in 2018 and 7.2 per cent higher than in 2010. World military spending rose in each of the five years from 2015, having decreased steadily from 2011 until 2014 following the global financial and economic crisis. This Fact Sheet highlights the regional and national military expenditure data for 2019 and trends over the decade 2010–19. The data is from the updated SIPRI Military Expenditure Database, which provides military expenditure data by country for the years 1949–2019.
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