Textual Analysis of Indonesia’s Identity in Instagram

Budaya tontonan dalam era visual ini membuka peluang bagi ideologi-ideologi tertentu untuk menyusup membangun sebuah pemahaman melalui tampilan foto. Akun instagram berbasis keindahan alam menjadi sebagian kecil wujud hegemoni Barat dalam membangun defi nisi tentang identitas Indonesia. Komposisi, warna dan objek foto yang seragam di antara banyak foto yang diunggah menunjukkan sebuah anomali yang patut untuk dikupas. Identitas Indonesia yang digambarkan oleh Mooi Indie di masa lalu, seolah terulang saat ini dengan tampilan visual yang serupa. Di mana menampilkan pantai, sawah dan gunung yang tenang dengan komposisi yang formal dan simetris. Realitas sosial Indonesia yang kompleks, dinamis dan kadang-kadang carut marut dibingkai sedemikian rupa sehingga menjadi sederhana, terstruktur dan seragam. Aspek-aspek lain yang tidak sesuai akan disamarkan atau bahkan dihilangkan sama sekali. Persamaan visual image antara Mooi Indie dan foto instagram adalah penekanan pada faktor geografi , bukan historis. Pelanggengan stereotip Indonesia diwariskan sejak dahulu hingga sekarang melalui The Next Mooi Indie, perbedaan keduanya hanya pada media, dari lukisan ke foto di media sosial

Seleksi Fitur Pada Pengelompokan Dokumen Dengan Random Projection - Gram Schmidt Orthogonalization dan Algoritma Harmony Search

Proses pengelompokan dokumen sangat tergantung pada keberadaan fitur kata tiap dokumen dan kemiripan antar fitur kata tersebut. Fitur kata pada suatu dokumen terkadang merupakan fitur noise, redundant, maupun fitur kata yang tidak relevan sehingga menyebabkan hasil akhir proses pengelompokan dokumen menjadi bias. Selain itu, pengelompokan dokumen dengan metode klasik kurang bisa menghasilkan kelompok dokumen yang mampu merepresentasikan kemiripan isi pada tiap-tiap kelompok dokumen. Pada penelitian ini diusulkan seleksi fitur pada pengelompokan dokumen dengan Random Projection Gram Schmidt Orthogonalization (RPGSO) dan Algoritma Harmony Search (HS). Dengan metode RPGSO akan didapatkan tingkat kepentingan tiap-tiap fitur kata untuk semua dokumen. Pada algoritma HS, dilakukan proses pengelompokan dokumen berdasarkan urutan fitur-fitur kata dari RPGSO dengan fitness function berupa Average Distance of Documents to the cluster Centroid (ADDC). Untuk mendapatkan kelompok dokumen dengan kriteria evaluasi yang paling baik, proses pengelompokan dokumen dengan algoritma HS ini diiterasi untuk jumlah fitur yang berbeda sesuai urutan yang dihasilkan dari proses RP-GSO. Uji coba dilaksanakan terhadap tiga buah dataset dokumen berita dengan evaluasi menggunakan kriteria F-Measure. Berdasarkan uji coba tersebut, metode usulan mampu menghasilkan kelompok dokumen dengan rata-rata F-Measure lebih tinggi 9.50% dibandingkan dengan menggunakan seluruh fitur. Uji coba juga menunjukkan bahwa kelompok dokumen yang dihasilkan dari metode usulan memiliki rata-rata F-Measure lebih tinggi 8.40% dibandingkan K-Means yang menggunakan Cosine Similarity, dan jika dibandingkan dengan K-Means yang menggunakan Euclidean Distance, metode usulan mampu menghasilkan kelompok dokumen dengan rata-rata F-Measure lebih tinggi sampai 120.05%. ====================================================================================== Document clustering procceses are depends on the quality of its term features and the similarity between those features. Sometimes the features of the document is a noise, redundant, or irrelevant and its cause the result of document clustering is bias. Furthermore, classical clustering method was unable to generate clusters of documents that is represent the similarity of its contents. In this study, we propose feature selection in document clustering using Random Projection Gram Schmidt Orthogonalization (RPGSO) and Harmony Search (HS) algorithm. With using RPGSO methods we will obtain the rank of term features of all documents. Then, we cluster the document based on the rank of features using HS algorithm with fitness function is Average Distance of Documents to the cluster Centroid (ADDC). To produce the clusters of documents which the best evaluation criteria, the clustering algorithm will be iterated for different number of features based on RPGSO rank. The methode has been tested to three datasets of news documents with F-Measure as evaluation criteria,. Based on the testing result, the proposed method generates clusters of documents with average of F-Measure criteria that 9.50% higher than use all features of documents in datasets.The testing result also shown that the proposed method generate clusters of documents with average of F-Measure criteria that 8.40% higher than K-Means method with Cosine Similarity and 120.05% higher than K-Means method with Euclidean Distance

Clustering and its Application in Requirements Engineering

Large scale software systems challenge almost every activity in the software development life-cycle, including tasks related to eliciting, analyzing, and specifying requirements. Fortunately many of these complexities can be addressed through clustering the requirements in order to create abstractions that are meaningful to human stakeholders. For example, the requirements elicitation process can be supported through dynamically clustering incoming stakeholders’ requests into themes. Cross-cutting concerns, which have a significant impact on the architectural design, can be identified through the use of fuzzy clustering techniques and metrics designed to detect when a theme cross-cuts the dominant decomposition of the system. Finally, traceability techniques, required in critical software projects by many regulatory bodies, can be automated and enhanced by the use of cluster-based information retrieval methods. Unfortunately, despite a significant body of work describing document clustering techniques, there is almost no prior work which directly addresses the challenges, constraints, and nuances of requirements clustering. As a result, the effectiveness of software engineering tools and processes that depend on requirements clustering is severely limited. This report directly addresses the problem of clustering requirements through surveying standard clustering techniques and discussing their application to the requirements clustering process

Quantifying the Impacts of Anthropogenic Emissions and Specific Infrastructures on Urban Air Quality

The interconnectivity between city infrastructure, energy and air quality is explored by evaluating the impact of environmental regulations, urban layout, and the transportation sector on air quality and energy use. Particular aspects of the research include assessing how controls have impacted aerosol acidity (which impacts health), linkages between energy, demographics, and how both airports and the use of autonomous and electric vehicles may impact on air quality. This research finds that while environmental regulations are effective in curbing pollution, as measured through decreases in fine particulate matter (PM2.5) emissions in the U.S., PM2.5 particles (aerosol) remain acidic. An implication of this is that it could be decades before changes in aerosol acidity, which is related to the toxicity and adverse health impacts of PM2.5, are seen. The research also found a strong statistical relationship between residential energy (electric and natural gas) consumption and socio-economic demographic (SED) factors for Zip Code Tabulated Areas (ZCTAs) in metropolitan Atlanta. However the electricity model exhibited high bias. Additional analyses found that electricity use is affected by the urban morphology of the roadways, with ZCTAs in high road density areas using more electricity The impacts of airports, mainly the Atlanta Hartsfield Jackson (ATL) on air quality, was examined using fine scale chemical transport modeling (CMAQ).CMAQ results are evaluated using ground-based and high resolution satellite-based observations from the TROPOspheric Monitoring Instrument (TROPOMI). TROPOMI's ability to provide consistent NO2 vertical column densities (VCDs) is assessed using the CMAQ results around two power plants. A 3D airport emission inventory from full flight operations is developed and compared against a base inventory with only surface airport operation emissions allocated to ATL. Results show that the magnitude and spatial extent of airport effects on air quality would be understated if only the base inventory is used for regulatory purposes. Lastly, we assess the efficacy of an electrified automated fleet of passenger cars on 2050 air quality in the US with a 2050 scenario where gasoline powered passenger cars emit lower levels of pollution than present day automobiles with CMAQ. We find that electric cars have advantages over future gasoline vehicles in terms of improving air quality, though the magnitude varies by species (O3, PM2.5). The overall implications of our findings is that policy, technology and urban infrastructure have a compounded effect on the efficacy of environmental regulations, air quality and energy use. Multiple factors should be considered when designing policies promoting equitable, sustainable cities.Ph.D

Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey

International audienceTraffic analysis is a compound of strategies intended to find relationships, patterns, anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic classification is a subgroup of strategies in this field that aims at identifying the application's name or type of Internet traffic. Nowadays, traffic classification has become a challenging task due to the rise of new technologies, such as traffic encryption and encapsulation, which decrease the performance of classical traffic classification strategies. Machine Learning gains interest as a new direction in this field, showing signs of future success, such as knowledge extraction from encrypted traffic, and more accurate Quality of Service management. Machine Learning is fast becoming a key tool to build traffic classification solutions in real network traffic scenarios; in this sense, the purpose of this investigation is to explore the elements that allow this technique to work in the traffic classification field. Therefore, a systematic review is introduced based on the steps to achieve traffic classification by using Machine Learning techniques. The main aim is to understand and to identify the procedures followed by the existing works to achieve their goals. As a result, this survey paper finds a set of trends derived from the analysis performed on this domain; in this manner, the authors expect to outline future directions for Machine Learning based traffic classification

Essays on sentiment: an analysis of the commercial real estate market

This thesis deals with the extraction, construction and analysis of commercial real estate (CRE) sentiment within Europe and the U.K. especially. The three empirical studies in this thesis may contribute to our understanding of the discipline. As I establish in the literature review, the analysis of commercial real estate sentiment still offers a lot of potential for further research. Since real estate markets are subject to sentiment swings, scholars and market participants should consider them in their market analysis. The first study establishes the need for sentiment consideration within the European real estate market. In order to justify the research of sentiment analysis, I have used different indirect and direct sentiment proxies and applied them in yield models for 80 different commercial property (sub-)markets within Europe. The statistical modification of different sentiment proxies is needed since not all European property markets offer direct sentiment measures. The results suggest, that the consideration of sentiment in a yield model framework adds significant information. I found, that CRE markets, which are assumed to be more liquid and developed, show a larger exposure to property specific sentiment measures. Markets, which are assumed to be less developed (i.e. Eastern European markets) on the other hand, have a larger exposure to more general macroeconomic sentiment indicators. The second study introduces a new method, which can be used to extract sentiment from text documents. The primary motivation for the use of text documents and the application of Natural Language Processing (NLP) methods lies in the fact that these documents are published much faster than other sentiment proxies. This allows extracting a much more accurate market sentiment. The second study should be understood as an introductory chapter to the method and the field of NLP. In total four different wordlists (AFINN, BING, NRC and TM) are used to extract the sentiment form various market reports for the CRE market in U.K. The study reveals that sentiment extracted from those documents, can be used to improve autocorrelated models. The last study uses those findings and applies different supervised learning methods. While the second study has produced sufficient results, the underlying text corpus of market reports has shown a series of insufficiencies. I have therefore, used a large dataset of more than 120,000 news articles, all concerning the British CRE market. Findings suggest, that the main issue of supervised learning algorithms is the appropriate classification of the different entities. I offer two approaches in order to construct robust sentiment indicators

EEG Signal Processing in Motor Imagery Brain Computer Interfaces with Improved Covariance Estimators

Desde hace unos años hasta la actualidad, el desarrollo en el campo de los interfaces cerebro ordenador ha ido aumentando. Este aumento viene motivado por una serie de factores distintos. A medida que aumenta el conocimiento acerca del cerebro humano y como funciona (del que aún se conoce relativamente poco), van surgiendo nuevos avances en los sistemas BCI que, a su vez, sirven de motivación para que se investigue más acerca de este órgano. Además, los sistemas BCI abren una puerta para que cualquier persona pueda interactuar con su entorno independientemente de la discapacidad física que pueda tener, simplemente haciendo uso de sus pensamientos. Recientemente, la industria tecnológica ha comenzado a mostrar su interés por estos sistemas, motivados tanto por los avances con respecto a lo que conocemos del cerebro y como funciona, como por el uso constante que hacemos de la tecnología en la actuali- dad, ya sea a través de nuestros smartphones, tablets u ordenadores, entre otros muchos dispositivos. Esto motiva que compañías como Facebook inviertan en el desarrollo de sistemas BCI para que tanto personas sin discapacidad como aquellas que, si las tienen, puedan comunicarse con los móviles usando solo el cerebro. El trabajo desarrollado en esta tesis se centra en los sistemas BCI basados en movimien- tos imaginarios. Esto significa que el usuario piensa en movimientos motores que son interpretados por un ordenador como comandos. Las señales cerebrales necesarias para traducir posteriormente a comandos se obtienen mediante un equipo de EEG que se coloca sobre el cuero cabelludo y que mide la actividad electromagnética producida por el cere- bro. Trabajar con estas señales resulta complejo ya que son no estacionarias y, además, suelen estar muy contaminadas por ruido o artefactos. Hemos abordado esta temática desde el punto de vista del procesado estadístico de la señal y mediante algoritmos de aprendizaje máquina. Para ello se ha descompuesto el sistema BCI en tres bloques: preprocesado de la señal, extracción de características y clasificación. Tras revisar el estado del arte de estos bloques, se ha resumido y adjun- tado un conjunto de publicaciones que hemos realizado durante los últimos años, y en las cuales podemos encontrar las diferentes aportaciones que, desde nuestro punto de vista, mejoran cada uno de los bloques anteriormente mencionados. De manera muy resumida, para el bloque de preprocesado proponemos un método mediante el cual conseguimos nor- malizar las fuentes de las señales de EEG. Al igualar las fuentes efectivas conseguimos mejorar la estima de las matrices de covarianza. Con respecto al bloque de extracción de características, hemos conseguido extender el algoritmo CSP a casos no supervisados. Por último, en el bloque de clasificación también hemos conseguido realizar una sepa- ración de clases de manera no supervisada y, por otro lado, hemos observado una mejora cuando se regulariza el algoritmo LDA mediante un método específico para Gaussianas.The research and development in the field of Brain Computer Interfaces (BCI) has been growing during the last years, motivated by several factors. As the knowledge about how the human brain is and works (of which we still know very little) grows, new advances in BCI systems are emerging that, in turn, serve as motivation to do more re- search about this organ. In addition, BCI systems open a door for anyone to interact with their environment regardless of the physical disabilities they may have, by simply using their thoughts. Recently, the technology industry has begun to show its interest in these systems, mo- tivated both by the advances about what we know of the brain and how it works, and by the constant use we make of technology nowadays, whether it is by using our smart- phones, tablets or computers, among many other devices. This motivates companies like Facebook to invest in the development of BCI systems so that people (with or without disabilities) can communicate with their devices using only their brain. The work developed in this thesis focuses on BCI systems based on motor imagery movements. This means that the user thinks of certain motor movements that are in- terpreted by a computer as commands. The brain signals that we need to translate to commands are obtained by an EEG device that is placed on the scalp and measures the electromagnetic activity produced by the brain. Working with these signals is complex since they are non-stationary and, in addition, they are usually heavily contaminated by noise or artifacts. We have approached this subject from the point of view of statistical signal processing and through machine learning algorithms. For this, the BCI system has been split into three blocks: preprocessing, feature extraction and classification. After reviewing the state of the art of these blocks, a set of publications that we have made in recent years has been summarized and attached. In these publications we can find the different contribu- tions that, from our point of view, improve each one of the blocks previously mentioned. As a brief summary, for the preprocessing block we propose a method that lets us nor- malize the sources of the EEG signals. By equalizing the effective sources, we are able to improve the estimation of the covariance matrices. For the feature extraction block, we have managed to extend the CSP algorithm for unsupervised cases. Finally, in the classification block we have also managed to perform a separation of classes in an blind way and we have also observed an improvement when the LDA algorithm is regularized by a specific method for Gaussian distributions