STRUKTUR NASKAH DRAMA ROH KARYA WISRAN HADI

The structural approach is an initial approach in a literary research. The purpose of this research is to describe the structural elements and describe the relationship between any elements in the drama script Roh by Wisran Hadi. The source of this research is the drama script Roh by Wisran Hadi written in June 1998 in Pagaruyung, West Sumatra. The script of this play has four acts with 29 pages. The drama script Roh is one of the best scripts by Wisran Hadi and won the 2nd place award in the 2003 DKJ competition. The drama script Roh by Wisran Hadi was published in the Sobrat drama script collection. Published by PT. Grasindo Jakarta 2003. The results of this study are in accordance with the initial objectives, the process of obtaining data is carried out through various stages, starting from the stage of recording data, classifying data and analyzing data. This research was conducted to prove that within the intrinsic elements of Wisran Hadi's dramae Roh, there is a concrete and significant relationship so that a literary work can be enjoyed in every verse of the written wordPendekatan struktur merupakan suatu pendekatan awal dalam sebuah penelitian sastra. Tujuan penelitian ini ialah untuk mendeskripsikan unsur-unsur struktur dan mendeskripsikan hubungan antar unsur apa saja yang ada dalam  naskah drama Roh karya Wisran Hadi. Sumber penelitian ini adalah naskah drama Roh karya Wisran Hadi yang ditulis pada Juni 1998 di Pagaruyung, Sumatera Barat. Naskah drama ini memiliki empat babak dengan 29 halaman. Naskah drama Roh merupakan salah satu naskah terbaik karya Wisran Hadi dan mendapatkan penghargaan juara II sayembara DKJ Tahun 2003. Naskah Drama Roh karya Wisran Hadi diterbitkan dalam kumpulan naskah drama Sobrat. Diterbitkan oleh PT. Grasindo Jakarta 2003. Hasil penelitian ini sesuai dengan tujuan awal, proses mendapatkan data dilakukan melalui berbagai tahap, mulai dari tahap pencatatan data, pengklasifikasian data serta menganalisis data. Penelitian ini dilaksanakan  untuk membuktikan bahwa di dalam unsur-unsur intrinsik naskah dramaeRoh Karya Wisran Hadi memiliki hubungan yang konkret dan signifikan sehingga terbentuklah suatu karya sastra yang dapat dinikmati setiap bait kata yang tertuli

STRUKTUR NASKAH DRAMA ROH KARYA WISRAN HADI

The structural approach is an initial approach in a literary research. The purpose of this research is to describe the structural elements and describe the relationship between any elements in the drama script Roh by Wisran Hadi. The source of this research is the drama script Roh by Wisran Hadi written in June 1998 in Pagaruyung, West Sumatra. The script of this play has four acts with 29 pages. The drama script Roh is one of the best scripts by Wisran Hadi and won the 2nd place award in the 2003 DKJ competition. The drama script Roh by Wisran Hadi was published in the Sobrat drama script collection. Published by PT. Grasindo Jakarta 2003. The results of this study are in accordance with the initial objectives, the process of obtaining data is carried out through various stages, starting from the stage of recording data, classifying data and analyzing data. This research was conducted to prove that within the intrinsic elements of Wisran Hadi's dramae Roh, there is a concrete and significant relationship so that a literary work can be enjoyed in every verse of the written wordPendekatan struktur merupakan suatu pendekatan awal dalam sebuah penelitian sastra. Tujuan penelitian ini ialah untuk mendeskripsikan unsur-unsur struktur dan mendeskripsikan hubungan antar unsur apa saja yang ada dalam  naskah drama Roh karya Wisran Hadi. Sumber penelitian ini adalah naskah drama Roh karya Wisran Hadi yang ditulis pada Juni 1998 di Pagaruyung, Sumatera Barat. Naskah drama ini memiliki empat babak dengan 29 halaman. Naskah drama Roh merupakan salah satu naskah terbaik karya Wisran Hadi dan mendapatkan penghargaan juara II sayembara DKJ Tahun 2003. Naskah Drama Roh karya Wisran Hadi diterbitkan dalam kumpulan naskah drama Sobrat. Diterbitkan oleh PT. Grasindo Jakarta 2003. Hasil penelitian ini sesuai dengan tujuan awal, proses mendapatkan data dilakukan melalui berbagai tahap, mulai dari tahap pencatatan data, pengklasifikasian data serta menganalisis data. Penelitian ini dilaksanakan  untuk membuktikan bahwa di dalam unsur-unsur intrinsik naskah dramaeRoh Karya Wisran Hadi memiliki hubungan yang konkret dan signifikan sehingga terbentuklah suatu karya sastra yang dapat dinikmati setiap bait kata yang tertuli

A Comparative Study of Two State-of-the-Art Feature Selection Algorithms for Texture-Based Pixel-Labeling Task of Ancient Documents

International audienceRecently, texture features have been widely used for historical document image analysis. However, few studies have focused exclusively on feature selection algorithms for historical document image analysis. Indeed, an important need has emerged to use a feature selection algorithm in data mining and machine learning tasks, since it helps to reduce the data dimensionality and to increase the algorithm performance such as a pixel classification algorithm. Therefore, in this paper we propose a comparative study of two conventional feature selection algorithms, genetic algorithm and ReliefF algorithm, using a classical pixel-labeling scheme based on analyzing and selecting texture features. The two assessed feature selection algorithms in this study have been applied on a training set of the HBR dataset in order to deduce the most selected texture features of each analyzed texture-based feature set. The evaluated feature sets in this study consist of numerous state-of-the-art texture features (Tamura, local binary patterns, gray-level run-length matrix, auto-correlation function, gray-level co-occurrence matrix, Gabor filters, Three-level Haar wavelet transform, three-level wavelet transform using 3-tap Daubechies filter and three-level wavelet transform using 4-tap Daubechies filter). In our experiments, a public corpus of historical document images provided in the context of the historical book recognition contest (HBR2013 dataset: PRImA, Salford, UK) has been used. Qualitative and numerical experiments are given in this study in order to provide a set of comprehensive guidelines on the strengths and the weaknesses of each assessed feature selection algorithm according to the used texture feature set

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

NELasso: Group-Sparse Modeling for Characterizing Relations Among Named Entities in News Articles

Named entities such as people, locations, and organizations play a vital role in characterizing online content. They often reflect information of interest and are frequently used in search queries. Although named entities can be detected reliably from textual content, extracting relations among them is more challenging, yet useful in various applications (e.g., news recommending systems). In this paper, we present a novel model and system for learning semantic relations among named entities from collections of news articles. We model each named entity occurrence with sparse structured logistic regression, and consider the words (predictors) to be grouped based on background semantics. This sparse group LASSO approach forces the weights of word groups that do not influence the prediction towards zero. The resulting sparse structure is utilized for defining the type and strength of relations. Our unsupervised system yields a named entities\u27 network where each relation is typed, quantified, and characterized in context. These relations are the key to understanding news material over time and customizing newsfeeds for readers. Extensive evaluation of our system on articles from TIME magazine and BBC News shows that the learned relations correlate with static semantic relatedness measures like WLM, and capture the evolving relationships among named entities over time

Nelasso: Group-Sparse Modeling For Characterizing Relations Among Named Entities In News Articles

Named entities such as people, locations, and organizations play a vital role in characterizing online content. They often reflect information of interest and are frequently used in search queries. Although named entities can be detected reliably from textual content, extracting relations among them is more challenging, yet useful in various applications (e.g., news recommending systems). In this paper, we present a novel model and system for learning semantic relations among named entities from collections of news articles. We model each named entity occurrence with sparse structured logistic regression, and consider the words (predictors) to be grouped based on background semantics. This sparse group LASSO approach forces the weights of word groups that do not influence the prediction towards zero. The resulting sparse structure is utilized for defining the type and strength of relations. Our unsupervised system yields a named entities\u27 network where each relation is typed, quantified, and characterized in context. These relations are the key to understanding news material over time and customizing newsfeeds for readers. Extensive evaluation of our system on articles from TIME magazine and BBC News shows that the learned relations correlate with static semantic relatedness measures like WLM, and capture the evolving relationships among named entities over time

Analysis of moving bottlenecks considering a triangular fundamental diagram

A significant number of research efforts have studied and analyzed the case in which a vehicle is moving slower than the traffic stream. This phenomenon, known as a moving bottleneck, results in a disruption of traffic flow and may significantly impact the traffic stream behavior upstream, downstream and abreast the slow moving vehicle. In this paper, a macroscopic approach for modeling moving bottlenecks is developed using microscopically derived data considering a triangular fundamental diagram. The passing flow rates of different moving bottleneck scenarios are determined using a previously developed microscopic model based on simulated data derived from the INTEGRATION software. Using the simulation results, an explicit expression of the bottleneck diagram, a flow-density relationship that defines the phenomenon macroscopically is proposed and the behavior of the traffic stream downstream and abreast the moving obstruction is depicted. It is demonstrated that the behavior of the traffic stream downstream of the slow vehicle as well as the acceleration behavior while passing is governed by the demand level. Such a result is coherent and consistent, to a significant extent, with two decades of research related to modeling moving bottlenecks and constitutes a potential feasible and more detailed description of the phenomenon in the case of a triangular fundamental diagram. Finally, it is noteworthy that the research subject of this paper could be considered as a first step in developing a numerical and practitioner-friendly framework for the analysis of moving bottlenecks that does not involve approaching the problem from its theoretical perspective

Exploiting Topical Perceptions Over Multi-Lingual Text For Hashtag Suggestion On Twitter

Microblogging websites, such as Twitter, provide seemingly endless amount of textual information on a wide variety of topics generated by a large number of users. Microblog posts, or tweets in Twitter, are often written in an informal manner using multi-lingual styles. Ignoring informal styles or multiple languages can hamper the usefulness of microblogging mining applications. In this paper, we present a statistical method for processing tweets according to users perceptions of topics and hashtags. Based on the non-classical notion of relatedness of vocabulary terms to topics in a corpus, which is quantified by discriminative term weights, our method builds a ranked list of terms related to hashtags. Subsequently, given a new tweet, our method can suggest a ranked list of hashtags. Our method allows enhanced understanding and normalization of users perceptions for improved information retrieval applications. We evaluate our method on a dataset of 14 million tweets collected over a period of 52 days. Results demonstrate that the method actually learns useful relationships between vocabulary terms and topics, and that the performance is better than a Naive Bayes suggestion system. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved