Racial Prejudice in Nicola Yoon's The Sun is Also a Star

Abstrak The Sun Is Also a Star menceritakan sebuah keluarga yang berasal dari Korea Selatan yang hidup miskin, Dae Hyun karakter ayah dalam novel ini mendapatkan bantuan dari sepupunya yang sudah hidup berkecukupan di New York, sepupunya berjanji akan membantunya. Dae Hyun bersama istrinya Min Soo memutuskan untuk pindah ke New York, Amerika. Karena mereka menjadi generasi imigan pertama dari Korea Selatan, membutuhkan banyak penyesesuaian yang harus mereka lakukan. Seiring berjalannya waktu, kehidupan masa lalu mereka mempengaruhi bagaimana cara mereka memberikan prasangka ras kepada anak-anak mereka. Charlie anak tertua mereka dan Daniel anak bungsu keluarga ini, terpaksa merasakan akibat dari prasangka ras yang terjadi dikeluraga mereka. Tujuan dari penelitian ini adalah untuk menampilkan prasangka ras yang terjadi dikeluarga immigrant Korea Selatan sebagai kambing hitam atas trauma orang tua mereka dalam The Sun is Also A Star karya Nicola Yoon. Metode yang digunakan adalah analisis tekstual, melalui narasi dan dialog dalam novel. Penelitian ini menunjukkan bahwa The Sun is Also A Star mengungkapkan penyebab dari prasangka rasial yang dialami Charlie dan Daniel adalah sumber sosial, mempertahankan identitas sosial, dan kambing hitam. Kata Kunci: prasangka ras, kambing hitam, sosial ekonomi    Abstract The Sun Is Also a Star tells about the life of poor family from South Korea, Dae Hyun as character father, has cousin who already well-being in New York, promises to help him. Dae Hyun and his wife Min Soo deciding move to New York, America. Because of they are the first generation of South Korean immigrant, they need to adapt as much as they can. After moving to New York, their past-life gave them impact how they give racial prejudice to their sons’. Charlie as the eldest son and Daniel as the youngest son in this family, willy-nilly they get the impact racial prejudice from their family. The purpose of this study is depicting racial prejudice happened in Korean immigrant family as scapegoating of their trauma in Nicola Yoon’s The Sun is Also A Star. The method using textual analysis through the narration and the dialogue inside the novel. This study showing that The Sun is Also A Star is revealing the causes of racial prejudice from Charlie and Daniel are societal source, maintaining societal identity, and scapegoating. Keywords: racial prejudice, scapegoating, socio-economic &nbsp

Seeking Sustainability for Computing

The talk will provide two perspectives on how sustainability is considered in computing. First, the impact computing has on energy consumption and on the environment will be discussed through the prism of past and prior research projects. Computing currently drives advances in all areas of science and engineering, generates efficiencies in industries, and dominates the creation and delivery of entertainment. Computing is also a significant consumer of energy accounting for 3% of the global usage. Data centers account of a third of this consumption, yet also provide a case where efficiencies in system design have limited the energy use increase despite considerable growth in computational efficiency. Second, the sustainability of scientific software and data will be discussed. Scientific computing is often driven by applications and libraries created by small research groups that aim to share their work, improve the replicability of the results and provide a tool for a larger research community. Faced with limited funding, lack of academic recognition, and waning interest, such efforts however are often unsuccessful in creating, maintaining and sustaining quality software. Aspects on how software and data products can be sustained will be discussed

Generational interdependencies in families: The MULTILINKS research programme

Background: We identify four research themes where MULTILINKS, a programme of research on intergenerational family ties funded through the Seventh Framework of the European Commission, has brought new and unique insights. Key premises of the MULTILINKS approach involved an emphasis on (1) both young and old in families, (2) the ways in which social policies structure interdependencies in families, and (3) the influence of historical, economic and cultural contexts. Methods: Our overview includes research done in the context of the MULTILINKS programme at large as well as the papers in this special collection. Results: Firstly, by combining macro and micro perspectives on intergenerational family constellations across Europe it has been possible to provide a more nuanced view than is common in conventional portrayals of family change. Secondly, by extending research to Eastern European countries, the programme has not only identified crucial regional differences in co-residential arrangements and intergenerational exchanges in families, but also shown that explanations of well-being differentials are similar in Eastern and Western Europe. Thirdly, by focusing on legal and policy frameworks regarding the division of care and financial responsibilities for the young and old between the family and the state, it has been possible to distinguish patterns in the degree to which national policies strengthen or weaken generational interdependencies in families. Fourthly, research conducted in the context of the MULTILINKS programme has demonstrated the usefulness of paying attention to preferences about family members' responsibilities for each other. Conclusion: Recognition of the key premises of MULTILINKS has led to challenging, critical insights on intergenerational family ties

Automated machine learning for analysis and prediction of vehicle crashes

This work discusses the study and development of a graphical interface and implementation of a machine learning model for vehicle traffic injury and fatality prediction for a specified date range and for a certain zip (US postal) code based on the New York City's (NYC) vehicle crash data set. While previous studies focused on accident causes, little insight has been offered into how such data may be utilized to forecast future incidents, Studies that have historically concentrated on certain road segment types, such as highways and other streets, and a specific geographic region, this study offers a citywide review of collisions. Using cutting-edge database and networking technology, a user-friendly interface was created to display vehicle crash series. Following this, a support vector machine learning model was built to evaluate the likelihood of an accident and the consequent injuries and deaths at the zip code level for all of NYC and to better mitigate such events. Using the visualization and prediction approach, the findings show that it is efficient and accurate. Aside from transportation experts and government policymakers, the machine learning approach deliver useful insights to the insurance business since it quantifies collision risk data collected at specific places

Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)

It is beneficial to develop an efficient machine-learning based method for addition using embedded hexadecimal digits. Through a comparison between human-developed machine learning model and models sampled through Neural Architecture Search (NAS) we determine an efficient approach to solve this problem with a final testing loss of 0.2937 for a human-developed model

KUALITAS PELAYANAN KESEHATAN PADA PENYINTAS THALASEMIA DI KOTA SUKABUMI

This study aims to determine the quality of health services for Thalassemia survivors in Sukabumi City. This study uses a qualitative research method with a narrative approach. The results of this study indicate that the Quality of Health Services for Thalassemia Survivors in Sukabumi City, especially at Rs. Bhayangkara Setukpa Sukabumi already has the following improvements: 1) Already has a special room for Thalassemia Survivors; 2) The chelation medicine already has various types of iron syrup, tablets, and brewed chelation medicine; 3) Have a special Thalassemia officer. As for when viewed based on the assessment of the dimensions of Reliability (Reliability), Responsiviness (Response/responsiveness), and Empathy (Empathy) health services provided by the apparatus/officers for Thalassemia Survivors are of high quality, but the dimensions of Tangibles (Intangible) and Assurance (Guarantee) are not qualified. or still not optimal, because the quality of health services provided is still not in accordance with the medical standards as outlined in the Regulation of the Minister of Health of the Republic of Indonesia Number 1109/MENKES/PER/VI/2011 concerning Technical Instructions for Guaranteed Thalassemia Treatment Services, especially in facilities and infrastructure. . The Sukabumi City Health Office provides a policy regarding the Health Services for Thalassemia Survivors in Sukabumi City, namely Thalassemia survivors participating in the National Health Insurance for the Healthy Indonesia Card (JKN KIS) do not need to go to the First Level Health Facility (FKTP) again to get referrals, and Thalassemia Sukabumi City has a halfway house as form of local government attention

Hyperspectral discrimination of tree species with different classifications using single- and multiple-endmember.

Discrimination of tree species with different ages is performed in three classifications using hyperspectral data. The first classification is between Broadleaves and pines; the second classification is between Broadleaves, Corsican Pines, and Scots Pines, and the third classification is between six tree species including different ages of Corsican and Scots Pines. These three classifications are performed by having single- and multiple-endmember and considering five different spectral measure techniques (SMTs) in combination with reflectance spectra (ReflS), first and second derivative spectra. The result shows that using single-endmember, derivative spectra are not useful for a more challenging classification. This is further emphasized in multiple-endmember classification, where all SMTs perform better in ReflS rather than derivative in all classifications. Furthermore, using derivative spectra, discrimination accuracy become more dependent on the type of SMTs, especially in single-endmember. By employing multiple-endmember, the within-species variation is significantly reduced, thereby, the remaining challenge in discriminating tree species with different ages is only due to the between-species similarity. Overall, discrimination accuracies around 92.4, 76.8, and 71.5% are obtained using original reflectance and multiple-endmember for the first, second, and third classification, which is around 14.3, 17, and 8.3% higher than what were obtained in single-endmember classifications, respectively. Also, amongst the five SMTs, Euclidean distance (in both single- and multiple-endmember) and Jeffreys–Matusita distance (in single-endmember and derivative spectra) provided the highest discrimination accuracies in different classifications. Furthermore, when discrimination become more challenging from the first to second and third classification, the performance difference between different SMTs is increased from 1.4 to 3.8 and 7.3%, respectively. The study shows high potential of multiple-endmember to be employed in remote sensing applications in the future for improving tree species discrimination accuracy

Band reduction for hyperspectral imagery processing

ABSTRACT Feature reduction denotes the group of techniques that reduce high dimensional data to a smaller set of components. In remote sensing feature reduction is a preprocessing step to many algorithms intended as a way to reduce the computational complexity and get a better data representation. Reduction can be done by either identifying bands from the original subset (selection), or by employing various transforms that produce new features (extraction). Research has noted challenges in both directions. In feature selection, identifying an "ideal" spectral band subset is a hard problem as the number of bands is increasingly large, rendering any exhaustive search unfeasible. To counter this, various approaches have been proposed that combine a search algorithm with a criterion function. However, the main drawback of feature selection remains the rather narrow bandwidths covered by the selected bands resulting in possible information loss. In feature extraction, some of the most popular techniques include Principal Component Analysis, Independent Component Analysis, Orthogonal Subspace Projection, etc. While they have been used with success in some instances, the resulting bands lack a physical relationship to the data and are mostly produced using statistical strategies. We propose a new technique for feature reduction that exploits search strategies for feature selection to extract a set of spectral bands from a given imagery. The search strategy uses dynamic programming techniques to identify 'the best set" of features

Chernoff Dimensionality Reduction-Where Fisher Meets FKT

Well known linear discriminant analysis (LDA) based on the Fisher criterion is incapable of dealing with heteroscedasticity in data. However, in many practical applications we often encounter heteroscedastic data, i.e., within-class scatter matrices can not be expected to be equal. A technique based on the Chernoff criterion for linear dimensionality reduction has been proposed recently. The technique extends well-known Fisher\u27s LDA and is capable of exploiting information about heteroscedasticity in the data. While the Chernoff criterion has been shown to outperform the Fisher\u27s, a clear understanding of its exact behavior is lacking. In addition, the criterion, as introduced, is rather complex, making it difficult to clearly state its relationship to other linear dimensionality reduction techniques. In this paper, we show precisely what can be expected from the Chernoff criterion and its relations to the Fisher criterion and Fukunaga-Koontz transform. Furthermore, we show that a recently proposed decomposition of the data space into four subspaces is incomplete. We provide arguments on how to best enrich the decomposition of the data space in order to account for heteroscedasticity in the data. Finally, we provide experimental results validating our theoretical analysis

Statistical Steganalyis of Images Using Open Source Software

Abstract-In this paper we present a novel steganalytic tool based on statistical pattern recognition. The main aim of our project was to design and implement a system able to classify the images into ones with no hidden message and steganographic images using classic pattern classification techniques such as Bayesian classification and decision trees. Experiments are conducted on a large data set of images to determine the classification algorithm that performs better by comparing classification success and error rates in each case. We have employed Weka, a data-mining tool developed in java for this purpose. We have also developed an application using Weka Java library for loading the data of the Images and classify the images into normal images and steganographic images. This application runs a GUI(Graphical User Interface) that enables the user to choose the classifier and other options required for the classification. Our results are aligned with current state of the art research and have the advantage of using open source software