Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: Exchange Rate Volatility and Trade among the Asia Pacific
Abstract: The purpose of this paper is to investigate the impact of exchange rate volatility on exports among 14 Asia Pacific countries, where various measures to raise the intra-region trade are being implemented. Specifically, this paper estimates a gravity model, in which the dependent variable is the product of the exports of two trading countries. In addition, it also estimates a unilateral exports model, in which the dependent variable is not the product of the exports of two trading countries but the exports from one country to another. By doing this, the depreciation rate of the exporting country's currency value can be included as one of the explanatory variables affecting the volume of exports. As the explanatory variables of the export volume, the gravity model adopts the product of the GDPs of two trading counties, their bilateral exchange rate volatility, their distance, a time trend and dummies for the share of the border line, the use of the same language, and the APEC membership. In the case of the unilateral exports model, the product of the GDPs is replaced by the GDP of the importing country, and the depreciation rate of the exporting country's currency value is dded. In addition, considering that the export volume will also depend on various onditions of the exporting country, dummies for exporting countries are also included as an explanatory variable. The empirical tests, using annual data for the period from 1980 to 2002, detect a significant negative impact of exchange rate volatility on the volume of exports. In addition, various tests using the data for sub-sample periods indicate that the negative impact had been weakened since 1989, when APEC had launched, and surged again from 1997, when the Asian financial crisis broke out. This finding implies that the impact of exchange rate volatility is time-dependent and that it is significantlynegative at least in the present time. This phenomenon is noticed regardless which estimation model is adopted. In addition, the test results show that the GDP of the importing country, the depreciation of the exporting country's currency value, the use of the same language and the membership of APEC have positive impacts on exports, while the distance between trading countries have negative impacts. Finally, it turns out that the negative impact of exchange rate volatility is much weaker among OECD countries than among non-OECD counties.
Extracted statement — has research problem: Exchange rate volatility
Research field: Science
Title: Exchange Rate Uncertainty in Turkey and its Impact on Export Volume
Abstract: This paper investigates the impact of real exchange rate volatility on Turkey’s exports to its most important trading partners using quarterly data for the period 1982 to 2001. Cointegration and error correction modeling approaches are applied, and estimates of the cointegrating relations are obtained using Johansen’s multivariate procedure. Estimates of the short-run dynamics are obtained through the error correction technique. Our results indicate that exchange rate volatility has a significant positive effect on export volume in the long run. This result may indicate that firms operating in a small economy, like Turkey, have little option for dealing with increased exchange rate risk.
Extracted statement — has research problem: Exchange rate volatility
Research field: Science
Title: Exchange Rate Volatility and Trade Flows of the U.K. in 1990s
Abstract: This paper examines the impact of exchange rate volatility on trade flows in the U.K. over the period 1990–2000. According to the conventional approach, exchange rate volatility clamps down trade volumes. This paper, however, identifies the existence of a positive relationship between exchange rate volatility and imports in the U.K. in the 1990s by using a bivariate GARCH-in-mean model. It highlights a possible emergence of a polarized version with conventional proposition that ERV works as an impediment factor on trade flows.
Extracted statement — has research problem: Exchange rate volatility
Research field: Science
Title: Precise eye localization through a general-to-specific model definition
Abstract: We present a method for precise eye localization that uses two Support Vector Machines trained on properly selected Haar wavelet coefficients. The evaluation of our technique on many standard databases exhibits very good performance. Furthermore, we study the strong correlation between the eye localization error and the face recognition rate.
Extracted statement — has research problem: Eye localization
Research field: Science
Title: Average of Synthetic Exact Filters
Abstract: This paper introduces a class of correlation filters called average of synthetic exact filters (ASEF). For ASEF, the correlation output is completely specified for each training image. This is in marked contrast to prior methods such as synthetic discriminant functions (SDFs) which only specify a single output value per training image. Advantages of ASEF training include: insensitivity to over-fitting, greater flexibility with regard to training images, and more robust behavior in the presence of structured backgrounds. The theory and design of ASEF filters is presented using eye localization on the FERET database as an example task. ASEF is compared to other popular correlation filters including SDF, MACE, OTF, and UMACE, and with other eye localization methods including Gabor Jets and the OpenCV cascade classifier. ASEF is shown to outperform all these methods, locating the eye to within the radius of the iris approximately 98.5% of the time.
Extracted statement — has research problem: Eye localization
Research field: Science
Title: Eye Localization based on Multi-Scale Gabor Feature Vector Model
Abstract: Eye localization is necessary for face recognition and related application areas. Most of eye localization algorithms reported thus far still need to be improved about precision and computational time for successful applications. In this paper, we propose an improved eye localization method based on multi-scale Gator feature vector models. The proposed method first tries to locate eyes in the downscaled face image by utilizing Gabor Jet similarity between Gabor feature vector at an initial eye coordinates and the eye model bunch of the corresponding scale. The proposed method finally locates eyes in the original input face image after it processes in the same way recursively in each scaled face image by using the eye coordinates localized in the downscaled image as initial eye coordinates. Experiments verify that our proposed method improves the precision rate without causing much computational overhead compared with other eye localization methods reported in the previous researches.
Extracted statement — has research problem: Eye localization
Research field: Science
Title: 2D cascaded AdaBoost for eye localization
Abstract: In this paper, 2D cascaded AdaBoost, a novel classifier designing framework, is presented and applied to eye localization. By the term "2D", we mean that in our method there are two cascade classifiers in two directions: The first one is a cascade designed by bootstrapping the positive samples, and the second one, as the component classifiers of the first one, is cascaded by bootstrapping the negative samples. The advantages of the 2D structure include: (1) it greatly facilitates the classifier designing on huge-scale training set; (2) it can easily deal with the significant variations within the positive (or negative) samples; (3) both the training and testing procedures are more efficient. The proposed structure is applied to eye localization and evaluated on four public face databases, extensive experimental results verified the effectiveness, efficiency, and robustness of the proposed method
Extracted statement — has research problem: Eye localization
Research field: Science
Title: A robust eye localization method for low quality face images
Abstract: Eye localization is an important part in face recognition system, because its precision closely affects the performance of face recognition. Although various methods have already achieved high precision on the face images with high quality, their precision will drop on low quality images. In this paper, we propose a robust eye localization method for low quality face images to improve the eye detection rate and localization precision. First, we propose a probabilistic cascade (P-Cascade) framework, in which we reformulate the traditional cascade classifier in a probabilistic way. The P-Cascade can give chance to each image patch contributing to the final result, regardless the patch is accepted or rejected by the cascade. Second, we propose two extensions to further improve the robustness and precision in the P-Cascade framework. There are: (1) extending feature set, and (2) stacking two classifiers in multiple scales. Extensive experiments on JAFFE, BioID, LFW and a self-collected video surveillance database show that our method is comparable to state-of-the-art methods on high quality images and can work well on low quality images. This work supplies a solid base for face recognition applications under unconstrained or surveillance environments.
Extracted statement — has research problem: Eye localization
Research field: Science
Title: Eye localization through multiscale sparse dictionaries
Abstract: This paper presents a new eye localization method via Multiscale Sparse Dictionaries (MSD). We built a pyramid of dictionaries that models context information at multiple scales. Eye locations are estimated at each scale by fitting the image through sparse coefficients of the dictionary. By using context information, our method is robust to various eye appearances. The method also works efficiently since it avoids sliding a search window in the image during localization. The experiments in BioID database prove the effectiveness of our method.
Extracted statement — has research problem: Eye localization
Research field: Science
Title: Enhanced Pictorial Structures for precise eye localization under incontrolled conditions
Abstract: In this paper, we present an enhanced pictorial structure (PS) model for precise eye localization, a fundamental problem involved in many face processing tasks. PS is a computationally efficient framework for part-based object modelling. For face images taken under uncontrolled conditions, however, the traditional PS model is not flexible enough for handling the complicated appearance and structural variations. To extend PS, we 1) propose a discriminative PS model for a more accurate part localization when appearance changes seriously, 2) introduce a series of global constraints to improve the robustness against scale, rotation and translation, and 3) adopt a heuristic prediction method to address the difficulty of eye localization with partial occlusion. Experimental results on the challenging LFW (Labeled Face in the Wild) database show that our model can locate eyes accurately and efficiently under a broad range of uncontrolled variations involving poses, expressions, lightings, camera qualities, occlusions, etc.
Extracted statement — has research problem: Eye localization
Research field: Science
Title: For your eyes only
Abstract: In this paper, we take a look at an enhanced approach for eye detection under difficult acquisition circumstances such as low-light, distance, pose variation, and blur. We present a novel correlation filter based eye detection pipeline that is specifically designed to reduce face alignment errors, thereby increasing eye localization accuracy and ultimately face recognition accuracy. The accuracy of our eye detector is validated using data derived from the Labeled Faces in the Wild (LFW) and the Face Detection on Hard Datasets Competition 2011 (FDHD) sets. The results on the LFW dataset also show that the proposed algorithm exhibits enhanced performance, compared to another correlation filter based detector, and that a considerable increase in face recognition accuracy may be achieved by focusing more effort on the eye localization stage of the face recognition process. Our results on the FDHD dataset show that our eye detector exhibits superior performance, compared to 11 different state-of-the-art algorithms, on the entire set of difficult data without any per set modifications to our detection or preprocessing algorithms. The immediate application of eye detection is automatic face recognition, though many good applications exist in other areas, including medical research, training simulators, communication systems for the disabled, and automotive engineering.
Extracted statement — has research problem: Eye localization
Research field: Science
Title: Automatic eye detection and its validation
Abstract: The accuracy of face alignment affects the performance of a face recognition system. Since face alignment is usually conducted using eye positions, an accurate eye localization algorithm is therefore essential for accurate face recognition. In this paper, we first study the impact of eye locations on face recognition accuracy, and then introduce an automatic technique for eye detection. The performance of our automatic eye detection technique is subsequently validated using FRGC 1.0 database. The validation shows that our eye detector has an overall 94.5% eye detection rate, with the detected eyes very close to the manually provided eye positions. In addition, the face recognition performance based on the automatic eye detection is shown to be comparable to that of using manually given eye positions.
Extracted statement — has research problem: Eye localization
Research field: Science
Title: Facial Expression Recognition using PCA and Gabor with JAFFE Database
Abstract: Abstract — In this paper I discussed Facial Expression Recognition System in two different ways and with two different databases. Principal Component Analysis is used here for feature extraction. I used JAFFE (Japanese Female Facial Expression). I implemented system with JAFFE database, I got accuracy of the algorithm is about 70-71% which gives quite poor Efficiency of the system. Then I implemented facial expression recognition system with Gabor filter and PCA. Here Gabor filter selected because of its good feature extraction property. The output of the Gabor filter was used as an input for the PCA. PCA has a good feature of dimension reduction so it was choose for that purpose.
Extracted statement — has research problem: Facial Expression Recognition
Research field: Science
Title: A Region Based Methodology for Facial Expression Recognition
Abstract: Facial expression recognition is an active research field which accommodates the need of interaction between humans and machines in a broad field of subjects. This work investigates the performance of a multi-scale and multi-orientation Gabor Filter Bank constructed in such a way to avoid redundant information. A region based approach is employed using different neighbourhood size at the locations of 34 fiducial points. Furthermore, a reduced set of 19 fiducial points is used to model the face geometry. The use of Principal Component Analysis (PCA) is evaluated. The proposed methodology is evaluated for the classification of the 6 basic emotions proposed by Ekman considering neutral expression as the seventh emotion.
Extracted statement — has research problem: Facial Expression Recognition
Research field: Science
Title: Jian-Cheng Huang “A New Facial Expression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA”
Abstract: ses a facial expression recognition system based on Gabor feature using a novel r bank. Traditionally, a global Gabor filter bank with 5 frequencies and 8 ten used to extract the Gabor feature. A lot of time will be involved to extract mensions of such Gabor feature vector are prohibitively high. A novel local Gabor art of frequency and orientation parameters is proposed. In order to evaluate the he local Gabor filter bank, we first employed a two-stage feature compression LDA to select and compress the Gabor feature, then adopted minimum distance nize facial expression. Experimental results show that the method is effective for eduction and good recognition performance in comparison with traditional entire . The best average recognition rate achieves 97.33% for JAFFE facial expression abor filter bank, feature extraction, PCA, LDA, facial expression recognition. deliver rich information about human emotion and play an essential role in human In order to facilitate a more intelligent and natural human machine interface of new cts, automatic facial expression recognition [1][18][20] had been studied world en years, which has become a very active research area in computer vision and n. There are many approaches have been proposed for facial expression analysis ages and image sequences [12][18] in the literature. we focus on the recognition of facial expression from single digital images with feature extraction. A number of approaches have been developed for extracting by: Motorola Labs Research Foundation (No.303D804372), NSFC (No.60275005), GDNSF 105938).
Extracted statement — has research problem: Facial Expression Recognition
Research field: Science
Title: Facial Expression Recognition from Line-Based Caricatures
Abstract: The automatic recognition of facial expression presents a significant challenge to the pattern analysis and man-machine interaction research community. Recognition from a single static image is particularly a difficult task. In this paper, we present a methodology for facial expression recognition from a single static image using line-based caricatures. The recognition process is completely automatic. It also addresses the computational expensive problem and is thus suitable for real-time applications. The proposed approach uses structural and geometrical features of a user sketched expression model to match the line edge map (LEM) descriptor of an input face image. A disparity measure that is robust to expression variations is defined. The effectiveness of the proposed technique has been evaluated and promising results are obtained. This work has proven the proposed idea that facial expressions can be characterized and recognized by caricatures.
Extracted statement — has research problem: Facial Expression Recognition
Research field: Science
Title: Robust Facial Expression Recognition Using Local Binary Patterns
Abstract: A novel low-computation discriminative feature space is introduced for facial expression recognition capable of robust performance over a rang of image resolutions. Our approach is based on the simple local binary patterns (LBP) for representing salient micro-patterns of face images. Compared to Gabor wavelets, the LBP features can be extracted faster in a single scan through the raw image and lie in a lower dimensional space, whilst still retaining facial information efficiently. Template matching with weighted Chi square statistic and support vector machine are adopted to classify facial expressions. Extensive experiments on the Cohn-Kanade Database illustrate that the LBP features are effective and efficient for facial expression discrimination. Additionally, experiments on face images with different resolutions show that the LBP features are robust to low-resolution images, which is critical in real-world applications where only low-resolution video input is available.
Extracted statement — has research problem: Facial Expression Recognition
Research field: Science
Title: Facial expression recognition and synthesis based on an appearance model
Abstract: Facial expression interpretation, recognition and analysis is a key issue in visual communication and man to machine interaction. We address the issues of facial expression recognition and synthesis and compare the proposed bilinear factorization based representations with previously investigated methods such as linear discriminant analysis and linear regression. We conclude that bilinear factorization outperforms these techniques in terms of correct recognition rates and synthesis photorealism especially when the number of training samples is restrained.
Extracted statement — has research problem: Facial Expression Recognition
Research field: Science
Title: The size, origins, and character of Mongolia’s informal sector during the transition
Abstract: The explosion of informal entrepreneurial activity during Mongolia's transition to a market economy represents one of the most visible signs of change in this expansive but sparsely populated Asian country. To deepen our understanding of Mongolia's informal sector during the transition, the author merges anecdotal experience from qualitative interviews with hard data from a survey of 770 informals in Ulaanbaatar, from a national household survey, and from official employment statistics. Using varied sources, the author generates rudimentary estimates of the magnitude of, and trends in, informal activity in Mongolia, estimates that are surprisingly consistent with each other. He evaluates four types of reasons for the burst of informal activity in Mongolia since 1990: 1) The crisis of the early and mid-1990s, during which large pools of labor were released from formal employment. 2) Rural to urban migration. 3) The"market's"reallocation of resources toward areas neglected under the old system: services such as distribution and transportation. 4) The institutional environments faced by the formal and informal sectors: hindering growth of the formal sector, facilitating entry for the informal sector. Formal labor markets haven't absorbed the labor made available by the crisis and by migration and haven't fully responded to the demand for new services. The relative ease of entering the informal market explains that market's great expansion. The relative difficulty of entering formal markets is not random but is driven by policy. Improving policies in the formal sector could afford the same ease of entry there as is currently being experienced in the informal sector.
Extracted statement — has research problem: Informal sector