نرم افزار های مورد نیاز

تبلیغات متنی


 

مشخصات مقاله
انتشار مقاله سال 2017
تعداد صفحات مقاله انگلیسی 23 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Matching pursuit parallel decomposition of seismic data
ترجمه عنوان مقاله تطبیق پیگیری داده های لرزه ای به صورت موازی
فرمت مقاله انگلیسی  PDF
رشته های مرتبط ژئوفیزیک
گرایش های مرتبط لرزه نگاری
مجله کامپیوترها و علوم زمین – Computers and Geosciences
دانشگاه China University of Geosciences (Beijing) – China
کلمات کلیدی پیگیری تطبیقی؛ سرعت محاسبه؛ الگوریتم موازی؛ داده های لرزه ای
کلمات کلیدی انگلیسی Matching pursuit; Computation speed; Parallel algorithm; Seismic data
شناسه دیجیتال – doi http://dx.doi.org/10.1016/j.cageo.2017.04.005
کد محصول E8150
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

بخشی از متن مقاله:
1. Introduction

Matching pursuit is an algorithm for sparse representation of signals. It adaptively decomposes a signal into a series of time-frequency atoms that match the time-frequency characteristics of the signal (Mallat and Zhang, 1993). The matching pursuit decomposition of seismic signals has been widely used in seismic data processing and interpretation, such as migration (Wang and Pann, 1996; Li et al, 1998), filtering (Nguyen and Castagna, 2000), interpolation (Øzbek, 2009), inversion (Yang et al, 2011; Zhou et al, 2013), and spectral analysis for hydrocarbon recognition (Castagna et al, 2003; Wang, 2007) and channel detection (Liu and Marfurt, 2007). The Gabor wavelet is used as the time-frequency atoms in a conventional matching pursuit algorithm. Considering the characteristics of seismic signals, Liu et al (2004) applies the Ricker wavelet as the time-frequency atoms to decompose seismic data. Liu and Marfurt (2005), and Wang (2007) implement matching pursuit decomposition of seismic signals based on the Morlet wavelet. A conventional matching pursuit algorithm costs a huge amount of computation, because it searches the optimal time-frequency atoms from an abundant dictionary (Mallat and Zhang, 1993). Liu and Marfurt (2005) introduce complex-trace analysis into the algorithm to avoid the blind search of time-frequency atoms, greatly improving its computation speed. Based on this, Wang (2007) summarizes “three-stage procedure” for matching pursuit decomposition of seismic signals where the decomposition is more precise by executing local search around the complex-trace attributes. In order to improve the spatial continuity of the decomposition, Wang (2010) further proposes multichannel matching pursuit. Although matching pursuit decomposition of seismic signals has been greatly improved, the local search of time-frequency atoms and iterative implementation of the algorithm still cost lots of time. To further enhance its execution speed, in addition to the algorithm itself, with the help of high-performance computers to accelerate the decomposition could also be considered. Liu and Marfurt (2007) perform matching pursuit to seismic data by searching a suite of optimal time-frequency atoms at one iteration rather than one at a time as in a conventional algorithm. Because the search of optimal time-frequency atoms has the same implementation and is independent of each other, Liu and Marfurt (2007) inspire us to execute matching pursuit decomposition in parallel by parallel computer systems. Hence, in this paper, we design a parallel decomposition algorithm of matching pursuit to effectively improve the computation speed of the matching pursuit decomposition of seismic signals. It gives full play to the advantages of parallel computing and has good expandability. We believe that the realization of matching pursuit parallel decomposition for seismic data will benefit the application of matching pursuit in the processing and interpretation of large-scale 3D seismic data.

نوشته مقاله انگلیسی رایگان در مورد تطبیق پیگیری داده های لرزه ای به صورت موازی – الزویر 2017 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 9 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Bootstrapping for multivariate linear regression models
ترجمه عنوان مقاله خودگردان سازی برای مدل های رگرسیون خطی چند متغیره
فرمت مقاله انگلیسی  PDF
رشته های مرتبط آمار
گرایش های مرتبط آمار ریاضی
مجله نامه های آمار و احتمال – Statistics and Probability Letters
دانشگاه Department of Biostatistics – Yale School of Public Health – USA
کلمات کلیدی بوت استرپ چند متغیره، مدل رگرسیون خطی چند متغیره، بوت استرپ باقی مانده
کلمات کلیدی انگلیسی Multivariate bootstrap, Multivariate linear regression model, Residual bootstrap
شناسه دیجیتال – doi https://doi.org/10.1016/j.spl.2017.11.001
کد محصول E8085
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

بخشی از متن مقاله:
1. Introduction

The linear regression model is an important and useful tool in many statistical analyses for studying the relationship among variables. Regression analysis is primarily used for predicting values of the response variable at interesting values of the predictor variables, discovering the predictors that are associated with the response variable, and estimating how changes in the predictor variables affects the response variable (Weisberg, 2005). The standard linear regression methodology assumes that the response variable is a scalar. However, it may be the case that one is interested in investigating multiple response variables simultaneously. One could perform a regression analysis on each response separately in this setting. Such an analysis would fail to detect associations between responses. Regression settings where associations of multiple responses are of interest require a multivariate linear regression model for analysis. Bootstrapping techniques are well understood for the linear regression model with a univariate response (Bickel and Freedman, 1981; Freedman, 1981). In particular, theoretical justification for the residual bootstrap as a way to estimate the variability of the ordinary least squares (OLS) estimator of the regression coefficient vector in this model has been developed (Freedman, 1981). Theoretical extensions of residual bootstrap techniques appropriate for the multivariate linear regression model have not been formally introduced. The existence of such an extension is stated without proof and rather implicitly in subsequent works (Freedman and Peters, 1984; Diaconis and Efron, 1983). In this article we show that the bootstrap procedures in Freedman (1981) provide consistent estimates of the variability of the OLS estimator of the regression coefficient matrix in the multivariate linear regression model. Our proof technique follows similar logic as Freedman (1981). The generality of the bootstrap theory developed in Bickel and Freedman (1981) provide the tools required for our extension to the multivariate linear regression model.

نوشته مقاله انگلیسی رایگان در مورد خودگردان سازی برای مدل های رگرسیون خطی – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 25 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله An RKHS model for variable selection in functional linear regression
ترجمه عنوان مقاله مدل RKHS برای انتخاب متغیر در رگرسیون خطی کارکردی
فرمت مقاله انگلیسی  PDF
رشته های مرتبط آمار
گرایش های مرتبط آمار ریاضی
مجله مجله تحلیل چندمتغیره – Journal of Multivariate Analysis
دانشگاه Departamento de Matem´aticas – Universidad Aut´onoma de Madrid – Spain
کلمات کلیدی انتخاب ویژگی، رگرسیون خطی تابعی، نقاط ضربه، انتخاب متغیر
کلمات کلیدی انگلیسی feature selection, functional linear regression, impact points, variable selection
شناسه دیجیتال – doi https://doi.org/10.1016/j.jmva.2018.04.008
کد محصول E8086
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

بخشی از متن مقاله:
1. Introduction:

statement of the problem and motivation The problem under study: variable selection in functional regression The study of regression models is clearly among the leading topics in statistics. In particular, these models play a central role in the theory of statistics with functional data, often called Functional Data Analysis (FDA); see [7, 15, 16] for an overview on FDA. Throughout this paper, we will consider “functional data” consisting of independent X1 = X1(t), . . . , Xn = Xn(t) observations (trajectories) drawn from a second-order (L 2 ) stochastic process X = X(t), t ∈ [0, 1], with continuous trajectories and continuous mean and covariance functions, denoted by m = m(t) and K(s, t), respectively. All the involved random variables are supposed to be defined in a common probability space (Ω, A, Pr). We are interested on functional regression models with scalar response, of type Yi = g(Xi) + εi , where g is a real function defined on a suitable space X where the trajectories of our process are supposed to live. The random variables εi are independent errors (and also independent from the Xi) with mean zero and common variance σ 2 . More specifically, we are concerned with variable selection issues; see, [4, Sec. 1], [11] for additional information and references. Basically, a variable selection functional method is an automatic procedure that takes a function {x(t), t ∈ [0, 1]} to a finite-dimensional vector (x(t1), . . . , x(tp)). The overall idea for variable selection is to choose the variables x(ti) (or, equivalently, the “impact points” t1, . . . , tp ∈ [0, 1]; see [22]), in an “optimal way” so that the original functional problem (regression, classification, clustering,…) is replaced with the corresponding multivariate version, based on the selected variables. In the regression setting, this would amount to replace the functional model Yi = g(Xi) + εi with a finite dimensional version of type Yi = φ{X(t1), . . . , X(tp)} + ei . Nevertheless, note that still the problem is of a functional nature, since the methods to select the ti are generally based upon the full data trajectories.

نوشته مقاله انگلیسی رایگان در مورد مدل RKHS برای انتخاب متغیر در رگرسیون خطی – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 46 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Consistent estimation of linear regression models using matched data
ترجمه عنوان مقاله برآورد مداوم مدل های رگرسیون خطی با استفاده از داده های همسان
فرمت مقاله انگلیسی  PDF
رشته های مرتبط آمار
گرایش های مرتبط آمار ریاضی
مجله مجله اقتصاد سنجی – Journal of Econometrics
دانشگاه Faculty of Economics – Setsunan University – Japan
کلمات کلیدی تصحیح تقاطع؛ استنتاج غیر مستقیم؛ رگرسیون خطی؛ برآورد تطبیقی؛ تصحیح خطای اندازه گیری
کلمات کلیدی انگلیسی Bias correction; indirect inference; linear regression; matching estimation; measurement error bias
شناسه دیجیتال – doi https://doi.org/10.1016/j.jeconom.2017.07.006
کد محصول E8087
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

بخشی از متن مقاله:
1 Introduction

Suppose that we are interested in estimating a linear regression model Y = β0 + X 0 1β1 + X 0 2β2 + Z 0 γ + u := W0 θ + u, E (u| W) = 0, (1) using a random sample, where X1 ∈ R d1 , X2 ∈ R d2 and Z ∈ R d3 . The reason for distinguishing between the regressors X1, X2 and Z will become clear shortly. In addition, while d1 = 0 is allowed, d2, d3 > 0 must be the case in our setup. When W = (1, X0 1 , X0 2 , Z0 ) 0 ∈ R d+1, where d := d1 + d2 + d3, is exogenous and a single random sample of (Y, X1, X2, Z) can be obtained, the ordinary least squares (OLS) estimator of θ = (β0, β0 1 , β0 2 , γ0 ) 0 is consistent. In reality, however, we often face the problem that (Y, X1, X2, Z) cannot be taken from a single data source. It is not uncommon that economists who use survey data for empirical analysis must collect all necessary variables from more than one source. Examples include Lusardi (1996), Bj¨orklund and J¨antti (1997), Currie and Yelowitz (2000), Dee and Evans (2003), Borjas (2004), Bover (2005), Fujii (2008), Bostic et al. (2009), and Murtazashvili et al. (2015), to name a few. Ridder and Moffitt (2007) provide an excellent survey. This is the setting in which we are interested. Specifically, suppose that instead of observing a complete data set (Y, X1, X2, Z), we have the following two overlapping subsets of data, (Y, X1, Z) and (X2, Z), i.e., some of the regressors are not available in the initial data set, where the initial data set is the one containing observations on the dependent variable along with a few other regressors. In such a setting, it is natural to construct a matched data set via exploiting the proximity of the common regressor(s) Z across the two samples. This is often called “probabilistic record linkage”. Here are two examples of the setting.

نوشته مقاله انگلیسی رایگان در مورد برآورد مداوم مدل های رگرسیون خطی – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 9 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Automated face recognition of rhesus macaques
ترجمه عنوان مقاله تشخیص چهره خودکار میمون رزوس
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط هوش مصنوعی
مجله مجله روش های علوم اعصاب – Journal of Neuroscience Methods
دانشگاه Institute of Neuroscience – Newcastle University – UK
کلمات کلیدی میمون، شناسایی چهره، تشخیص چهره، دید کامپیوتری
کلمات کلیدی انگلیسی Monkey, Face detection, Face recognition, Computer vision
شناسه دیجیتال – doi https://doi.org/10.1016/j.jneumeth.2017.07.020
کد محصول E8088
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

بخشی از متن مقاله:
1. Introduction

Automated methods for monitoring behavior of laboratory animals such as rodents and zebrafish are becoming widespread (Bains et al., 2016; Nema et al., 2016; Steele et al., 2007). They allow the monitoring of behavior effects in response to experi mental manipulations such as drugs, lesions, genetic modifications and disease. Concurrently similar automated behavior systems are being developed for monitoring health and welfare in a range of species including farm and laboratory animals (Roughan et al., 2009; Rushen et al., 2012). Rhesus macaques are one of the most common non-human primate species used in biomedical research including neuroscience research but to date the use of automated systems with non-human primates has been limited. A major challenge in measuring the behavior of any grouphoused animal is to reliably identify an individual animal. With macaques this is not an issue if the animals are singly-housed but welfare concerns are driving a move towards pair- and grouphousing of non-human primates in many countries. One solution is to add a tracking device to each animal; for example these could be colored jackets (Rose et al., 2012) or collars (Ballesta et al., 2014) in combination with video monitoring or electronic devices such as RFID tags (Maddali et al., 2013). These require regular handling of the animals and it is not currently known how the use of jackets and collars affects the behavior of the animals (personal observations with rhesus macaques suggest that the use of jackets can drastically reduce social behaviors such as grooming). Another solution is to use biometric identification based on the distinguishing visual characteristics of that species (e.g. coat pattern; Kühl and Burghardt, 2016). This has the advantage of being non-invasive. Rhesus macaques, in common with many primate species, do not have obvious individually identifiable features but the macaques themselves are capable of recognizing conspecifics by their faces (Parr et al., 2000). Face recognition technology has already been used in several non-human primate species including guenons (Allen and Higham, 2015), chimpanzees (Freytag et al., 2016) and gorillas (Loos, 2012) but not rhesus macaques. Freytag et al. (2016) achieved success rates of over 90% with images of captive chimpanzees. Face recognition technology was originally developed for use with humans and is becoming commonplace in daily life. Uses include automatic passport gates at airports, tagging of faces in photos on Facebook and use of facial image to unlock smart phones. Many of the early techniques focused on either reducing the dimensionality of the facial image or on extracting a particular feature from the image and then on classifying this output. Some of these methods for face recognition include EigenFaces (based on principal component analysis) and FisherFaces (based on linear discriminant analysis; Belhumeur et al., 1997). Some of the main challenges facing any face recognition system are coping with changes in light intensity and pose. A method based on local binary patterns (Ahonen et al., 2006) has been shown to be relatively robust to changes in light intensity. Most recently deep learning techniques have been applied to face recognition with a high level of success (Freytag et al., 2016 for chimpanzees; Parkhi et al., 2015 for humans).

نوشته مقاله انگلیسی رایگان در مورد تشخیص چهره خودکار میمون رزوس – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 32 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Face recognition based on recurrent regression neural network
ترجمه عنوان مقاله تشخیص چهره بر اساس رگرسیون مجدد شبکه عصبی
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط هوش مصنوعی
مجله محاسبات عصبی – Neurocomputing
دانشگاه Key Laboratory of Child Development and Learning Science of Ministry of Education – Southeast University – China
کلمات کلیدی رگرسیون مجدد شبکه عصبی (RRNN)، تشخیص چهره، یادگیری عمیق
کلمات کلیدی انگلیسی Recurrent regression neural network (RRNN), face recognition, deep learning
شناسه دیجیتال – doi https://doi.org/10.1016/j.neucom.2018.02.037
کد محصول E8089
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

بخشی از متن مقاله:
1. Introduction

Face recognition is a classic topic in past decades and now still attracts much attention in the field of computer vision and pattern recognition. Face recognition has a great potential in multimedia applications, e.g. video surveillance, personal identification, digital entertainment and so on [1, 2, 3, 4, 5]. With the rapid development of electric equipment techniques, more and more face images can be easily captured in the wild, especially videos from cameras of surveillance or cell phones. Therefore, video or image set based face recognition becomes more important in most of real-world applications and also becomes a popular topic in face analysis more recently. As face images captured from the unconstrained conditions are usually with complex appearance variations in poses, expressions, illuminations, etc., the existing face recognition algorithms still suffer from a severe challenge in fulfilling real applications to large-scale data scenes, although the current deep learning techniques have made a great progress on the unconstrained small face dataset, e.g., the recent success of deep learning methods on Labeled Faces in the Wild (LFW) [6]. In the task of face recognition, however, we cannot bypass this question of pose variations, which has been extensively studied and explored in past decades, and has not been well-solved yet. The involved methods may be divided into 20 3D [7, 8, 9] and 2D methods [10, 11, 12, 13, 14, 15, 16]. Since pose variations are basically caused by 3D rigid motions of face, 3D methods are more intuitive for pose generation. But 3D methods usually need some 3D data or recovery of 3D model from 2D data which is not a trivial thing. Moreover, the inverse transform from 3D model to 2D space is sensitive to facial appearance varia tions. In contrast to 3D model, due to decreasing one degree of freedom, 2D methods usually attempt to learn some transforms across poses, including linear models [17] or non-linear models [10, 18]. Because of its simplicity, 2D model has been widely used to deal with cross-pose face recognition with a comparable performance with 3D model. However, in many real scenes of face image sets, e.g., face video sequences, the changes of poses may be regarded as a nearly continuous stream of motions, while the existing methods usually neglect or do not make full use of this prior. Moreover, the pose variation is not the only factor between different images even for the same subject, which involves other complex factors.

نوشته مقاله انگلیسی رایگان در مورد تشخیص چهره بر اساس رگرسیون مجدد شبکه عصبی – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 15 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Learning context and the other-race effect: Strategies for improving face recognition
ترجمه عنوان مقاله زمینه یادگیری و تاثیر نسل دیگر: استراتژی برای بهبود تشخیص چهره
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط هوش مصنوعی
مجله تحقیق بصری – Vision Research
دانشگاه The University of Texas at Dallas – USA
کلمات کلیدی شناسایی چهره، تاثیر نسل دیگر، آموزش، زمینه یادگیری
کلمات کلیدی انگلیسی Face recognition, Other-race effect, Training, Learning context
شناسه دیجیتال – doi https://doi.org/10.1016/j.visres.2018.03.003
کد محصول E8090
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

بخشی از متن مقاله:
1. Introduction

The other-race effect (ORE) is the tendency for people to recognize faces of their “own” race more accurately than faces of “other” races (cf., Meissner & Brigham, 2001). Often characterized as an inability to discriminate or individualize other-race faces, the ORE suggests that there are clear differences in how own- and other-race faces are processed. The ORE has been studied extensively in face perception. Its effects have been examined across a variety of racial and ethnic groups, using multiple paradigms, (Meissner & Brigham, 2001), and more recently in face recognition algorithms (Phillips, Jiang, Narvekar, Ayyad, & O’Toole, 2011). Additionally, the ORE has been found in infants as young as three months (Sangrigoli & de Schonen, 2004), in children (Anzures et al., 2014; Pezdek, Blandon-Gitlin, & Moore, 2003; Tham, Bremner, & Hay, 2017), and in non-typically developing populations, such as individuals with schizophrenia (Pinkham et al., 2008) and autism spectrum disorder (Wilson, Palermo, Burton, & Brock, 2011; Yi et al., 2016). In recent research, there has been new emphasis on ways to improve face recognition (Heron-Delaney et al., 2011; Hugenberg, Miller, & Claypool, 2007; Ritchie & Burton, 2016; Rodríguez, Bortfeld, & Gutiérrez-Osuna, 2008; Tanaka & Pierce, 2009; White, Kemp, Jenkins, & Burton, 2014; Xiao et al., 2015). Several of these studies have explored ways to reduce the ORE. For example, participant awareness of the ORE (Hugenberg et al., 2007) and viewing caricatured images of faces (Rodríguez et al., 2008) reduce the ORE. Furthermore, developmental studies show that perceptual training in infants can prevent an ORE from emerging (Heron-Delaney et al., 2011). The effects of perceptual training on learning other-race faces have been studied also in children (Xiao et al., 2015) and adults (Tanaka & Pierce, 2009). For example, Xiao et al. (2015) examined the influence of individuation and categorization training of other-race faces for preschool children. Recognition performance was measured in the context of implicit racial bias. When trained to individuate African American faces, Chinese preschoolers demonstrated a reduced implicit bias for other-race faces (they were less likely to categorize an angry racially ambiguous face as African American). These results suggest that individuation training reduces implicit bias, which is a potential step towards reducing the ORE. Tanaka and Pierce (2009) investigated the effect of individuation and categorization training of Hispanic and African American faces on Caucasian adults. Participants in the individuation condition showed marginally better recognition than those in the categorization condition. These results suggest that individuation training may reduce the ORE. In addition to the focus on individuation training, repetitive and variable-image learning have been tested as factors that may improve recognition. Repeated exposure to a single image has been shown to increase recognition accuracy (cf., for a meta-analysis of these effects, Shapiro & Penrod, 1986). Recently, the importance of within-person variability has been considered as a factor in face recognition (Dowsett, Sandford, & Burton, 2016; Jenkins, White, Van Montfort, & Burton, 2011; Murphy, Ipser, Gaigg, & Cook, 2015). The human ability to “see” multiple, variable images of the same person as a single identity has been referred to in the literature as the ability to “tell people together” (Andrews, Jenkins, Cursiter, & Burton, 2015; Jenkins et al., 2011). This contrasts to the oft-cited human ability to “tell faces apart”, which is long thought to be the core of human face expertise for faces. It is generally understood now that the former is a characteristic of familiar face perception, whereas the latter applies to both familiar and unfamiliar face processing.

نوشته مقاله انگلیسی رایگان در مورد زمینه یادگیری و تاثیر نسل دیگر – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2017
تعداد صفحات مقاله انگلیسی 48 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه امرالد
نوع مقاله ISI
عنوان انگلیسی مقاله Depression and social anxiety in relation to problematic smartphone use: the prominent role of rumination
ترجمه عنوان مقاله افسردگی و نگرانی اجتماعی در رابطه با استفاده مشکل ساز از گوشی هوشمند: نقش برجسته نشخواری ذهنی
فرمت مقاله انگلیسی  PDF
رشته های مرتبط روانشناسی، پزشکی
گرایش های مرتبط روانشناسی عمومی، روانپزشکی
مجله تحقیق اینترنتی – Internet Research
دانشگاه Department of Psychology – University of Toledo – Toledo – USA
کلمات کلیدی افسردگی؛ اضطراب اجتماعی؛ نشخوار؛ نظریه ارتباطات؛ استفاده از تکنولوژی Problematic، اعتیاد به تلفن هوشمند؛ استفاده از تلفن هوشمند اعتیاد به اینترنت
کلمات کلیدی انگلیسی Depression; Social Anxiety; Rumination; Communication Theory; Problematic technology use; Smartphone addiction; Smartphone use; Internet addiction
کد محصول E8028
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

بخشی از متن مقاله:
Introduction

In recent years, published studies have examined the construct of problematic smartphone use. Problematic smartphone use is often defined as excessive use of a smartphone, with social or occupational functional impairment, and including dependence and symptoms seen in addictive disorders such as withdrawal and tolerance (Billieux et al., 2015a). Research has examined relations between psychopathology and problematic smartphone use and/or the continuum of smartphone use frequency. Depression and anxiety severity in particular are well-supported in association with problematic smartphone use and use frequency (reviewed in Elhai et al., 2017a). However, little research has explored the role of more contemporary transdiagnostic constructs of psychopathology in studying these relationships. The most commonly studied psychopathology constructs in relation to problematic smartphone use and usage frequency include variables measuring levels of depression, anxiety, stress and low self-esteem (reviewed in Elhai et al., 2017a). Depression and anxiety severity have demonstrated moderate and small links (respectively) to levels of problematic smartphone use and usage frequency, with effect sizes of .30 to .50 for depression severity (recently in Demirci et al., 2015; Lu et al., 2011; Smetaniuk, 2014), and approximately .20 for anxiety severity (recently, Demirci et al., 2015; Elhai et al., 2016; Kim, R et al., 2015; Lee, Y-K et al., 2014). These findings generally parallel those from the literature on internet addiction (but not specifically smartphone use) (Ho et al., 2014; Prizant-Passal et al., 2016). However, effect sizes are nonetheless small on average for anxiety, and in some studies for depression severity (Augner and Hacker, 2012; Elhai et al., 2016, 2017b; Kim, J et al., 2015). Some evidence suggests a bidirectional relationship, whereby problematic smartphone use can lead to depression and anxiety severity, and vice-versa (van Deursen et al., 2015; Yen et al., 2012). Theory and empirical evidence demonstrate a dual system process that underlies addictive disorders, involving a balance between impulsive reward seeking and reflection/inhibition (Bechara, 2005; Volkow and Fowler, 2000). This theory has been supported in research on problematic use of technology, and suggests that deficits in brain circuitry may be responsible for such problematic use (Turel and Qahri-Saremi, 2016; Turel et al., 2016). Relevant to the present paper, depression and anxiety severity, and rumination, may be caused by the same types of brain circuitry deficits found in the addictive disorders. Furthermore, research on problematic smartphone use thus far has not examined more contemporary “transdiagnostic” psychopathology constructs – that is, constructs which cut across numerous mental disorders. Such constructs are increasingly important in understanding mechanisms involved in the etiology and maintenance of psychopathology (Mansell et al., 2008). Mediating and moderating variables between psychopathology and problematic internet use (albeit not specific to smartphone) have been tested and supported recently (Brand et al., 2016; Jiang, 2014).

نوشته مقاله انگلیسی رایگان در مورد نگرانی اجتماعی در رابطه با استفاده از گوشی هوشمند – امرالد 2017 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 64 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه الزویر
نوع مقاله ISI
عنوان انگلیسی مقاله Opposite molecular signatures of depression in men and women
ترجمه عنوان مقاله اثرات متضاد مولکولی افسردگی در مردان و زنان
فرمت مقاله انگلیسی  PDF
رشته های مرتبط روانشناسی، پزشکی
گرایش های مرتبط روانشناسی عمومی، روانپزشکی
مجله روانپزشکی بیولوژیکی – Biological Psychiatry
دانشگاه Department of Psychiatry – University of Pittsburgh Medical School – USA
کد محصول E8029
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

بخشی از متن مقاله:
Introduction

Major depressive disorder (MDD) is a leading cause of disability worldwide (1), but its impact differs substantially between sexes. Women are twice as likely to be diagnosed with a single MDD episode, and four times more likely to be diagnosed with recurrent MDD (e.g., (2-7). Women with MDD also report greater illness severity, more symptoms (3, 8-10), and different symptomatology than men. For instance, women are three times more likely to have atypical depression, characterized by hypersomnia and weight gain (11-15). Comorbidity of MDD with other disorders also differs between sexes. For instance, women are more likely to have comorbid anxiety disorders, whereas men are more likely to have comorbid substance use disorders (e.g., (16-19)). Some studies suggest that women have more positive treatment outcomes with selective serotonin reuptake inhibitors and monoamine oxidase inhibitors (20, 21), whereas men seem to respond better to tricyclic antidepressants. Research suggests dysfunction of the corticolimbic network of mood regulation in MDD. We consider three network nodes, the dorsolateral prefrontal cortex (DLPFC; Brodmann area 9 (BA9)), subgenual anterior cingulate cortex (ACC; BA25), and amygdala (AMY). Structural and functional neuroimaging implicates these regions in MDD [e.g. (22-30)]. Since some studies were performed in only women (24, 31), it is unclear whether results are generalizable to both sexes. Additionally, studies that included both sexes often lacked statistical power to stratify by sex. The idea that these brain regions are differentially affected in men and women with MDD is supported by sex differences in activation during normal emotional states. fMRI studies of non-depressed subjects suggest differential regional during emotion-related tasks, with women having more AMY activation and men more cortical activation [e.g., (32-34)]. Postmortem brain studies report reduced density and number of glial cells in MDD in the DLPFC (35), ACC (36, 37), and AMY (38, 39). Additionally, there is reduced neuron size in DLPFC (36) and ACC (37) in MDD. However, these analyses were not stratified by sex. Gene expression studies on tissue homogenate from postmortem brains have identified sex differences in MDD.

نوشته مقاله انگلیسی رایگان در مورد اثرات متضاد مولکولی افسردگی در مردان و زنان – الزویر 2018 اولین بار در دانلود مقالات ISI. پدیدار شد.


 

مشخصات مقاله
انتشار مقاله سال 2017
تعداد صفحات مقاله انگلیسی 6 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه IEEE
نوع مقاله ISI
عنوان انگلیسی مقاله Event Detection on Large Social Media Using Temporal Analysis
ترجمه عنوان مقاله تشخیص رویداد رسانه اجتماعی عظیم با استفاده از تجزیه و تحلیل موقت
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی کامپیوتر، فناوری اطلاعات
گرایش های مرتبط هوش مصنوعی، اینترنت و شبکه های گسترده
مجله هفتمین کارگاه و کنفرانس محاسبات و ارتباطات – 7th Annual Computing and Communication Workshop and Conference
دانشگاه School of Engineering – University of Bridgeport – Connecticut
کلمات کلیدی کلان داده، داده کاوی، تحلیل رسانه های اجتماعی، تشخیص رویداد، یادگیری ماشین
کلمات کلیدی انگلیسی Big data, data mining, Social media analysis, Event detection, Machine learning
کد محصول E8030
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

بخشی از متن مقاله:
1. Introduction

Social media networks had become very popular recently. Statistica, the online statistics portal, estimated that there are 2.22 billion active social network users by the end of the year 2016 [1]. The same source estimates that this number will increase to 2.72 billion active social network user around the globe by the end of year 2019. Publishing personal contents has never been easier with the wide availability of microblogging platforms such as Twitter [2]. This has enabled users to post their opinions swiftly [3]. Recent research shows that Twitter process more than 500 million tweets daily [4]. The number of tweets that has been sent since 2006 when Twitter was founded is more than 300 billion tweets [5]. Data generated by social media users is huge in volume, grows at a very high velocity, varies in its type, and varies in its quality. These characteristics, also called the four V’s, i.e volume, velocity, variety, and veracity, are the main dimensions that characterize big data [6]. The availability of huge datasets representing more than a quarter of the world’s population who are actively interacting creates an opportunity to uncover patterns that could explain a lot of social phenomena [7], [8]. Meanwhile, the availability of these datasets introduces many challenges for researchers who are trying to analyze and process such data [7], [9].

1.1. Research Problems

Social media networks are now considered as one of the major news channels. Mainstream media tend to monitor social media networks by looking for breaking news and interesting event. Furthermore, government entities are also relying on social media for the purpose of collecting security related intelligence. On the other hand, social network analysis focuses on individual users and their networks. The problem, i.e. the event detection on social media, attracted researchers attention recently because of the enormous popularity of social media. Existing approaches focus on features that doesn’t reflect the characteristic of the social network. Therefore, it fails to detect events in the context of the social network as a whole, which result in lower accuracy in detecting events. To address the problem, the temporal approach for processing a social network as we can detect an event from multiple temporal images. We define an event as an occurrence that has enough force and momentum that could create an observable change the shape of social network. We can measure such change by comparing the shape of data as time goes by. Therefore, if the shape of data at time t1 is different from the shape of data at time t2 we can conclude that there was a certain event that has an impact on the data and changed its shape. In this study, we show that processing temporal social networks graphs captures the complete complexity of the social network, which results in a higher accuracy of event detection model. We propose a temporal social network graphs event detection framework based on which we propose a novel social network transformation approach that transforms social media streams into temporal images. This allows for building a better event detection predictive model. We validate the proposed approach by performing experiments on streamed social media data collected for the purpose of this research. The ground truth collected data is extracted from mainstream media and labeled the dataset to create training and testing data. We achieve an accuracy rate in detecting events that surpasses existing approaches. We evaluate our proposed approach by using commonly used model evaluation metrics. Accuracy alone could be deceiving especially when data is imbalance. We calculated and compared precision, recall, and F1-score. We also used precision-recall and ROC curves to evaluate the performance of our proposed approach.

نوشته مقاله انگلیسی رایگان در مورد تشخیص رویداد رسانه اجتماعی عظیم با تحلیل موقت – IEEE 2017 اولین بار در دانلود مقالات ISI. پدیدار شد.

آخرین دیدگاه‌ها

    دسته‌ها