This thesis aims to explore and describe the content diversity of Siri’s search results in the polarized context of US politics. To do so, a crowdsourced audit was conducted. A diverse sample of 134 US-based Siri users between the ages of 18-64 performed five identical queries about the politically controversial issues of gun laws, immigration, the death penalty, taxes and abortion. The data were viewed through a theoretical framework using the concepts of algorithmic bias and media-centric fragmentation. The results suggest that Siri’s search algorithm produces a long tail distribution of search results: Forty-two percent of the participants received the six most frequent answers, while 22% of the users received unique answers. These statistics indicate that Siri’s search algorithm causes moderate concentration and low fragmentation. The age and, surprisingly, the political orientation of users, do not seem to be driving either concentration or fragmentation. However, the users' gender and location appears to cause low concentration. The finding that Siri’s search algorithm produces a long tail of replies challenges previous research on the content diversity of search results, which found no evidence of fragmentation. However, due to the limited scope of this study, these findings cannot be generalized to a larger population. Further research is needed to support or refute them.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Online social media has made the process of disseminating news so quick that people have shifted their way of accessing news from traditional journalism and press to online social media sources. The rapid rotation of news on social media makes it challenging to evaluate its reliability. Fake news not only erodes public trust but also subverts their opinions. An intelligent automated system is required to detect fake news as there is a tenuous difference between fake and real news. This paper proposes an intelligent gravitational search random forest (IGSRF) algorithm to be employed to detect fake news. The IGSRF algorithm amalgamates the Intelligent Gravitational Search Algorithm (IGSA) and the Random Forest (RF) algorithm. The IGSA is an improved intelligent variant of the classical gravitational search algorithm (GSA) that adds information about the best and worst gravitational mass agents in order to retain the exploitation ability of agents at later iterations and thus avoid the trapping of the classical GSA in local optimum. In the proposed IGSRF algorithm, all the intelligent mass agents determine the solution by generating decision trees (DT) with a random subset of attributes following the hypothesis of random forest. The mass agents generate the collection of solutions from solution space using random proportional rules. The comprehensive prediction to decide the class of news (fake or real) is determined by all the agents following the attributes of random forest. The performance of the proposed algorithm is determined for the FakeNewsNet dataset, which has sub-categories of BuzzFeed and PolitiFact news categories. To analyze the effectiveness of the proposed algorithm, the results are also evaluated with decision tree and random forest algorithms. The proposed IGSRF algorithm has attained superlative results compared to the DT, RF and state-of-the-art techniques. / Revisión por pares

No description available.

No description available.

No description available.

<div>The contribution of this research work can be divided into two main tasks: 1) implementing this Electronic Warfare Asset Allocation Problem (EWAAP) with the Genetic Algorithm (GA); 2) Comparing performance of Genetic Algorithm to Particle Swarm Optimization (PSO) algorithm. This research problem implemented Genetic Algorithm in C++ and used QT Data Visualization for displaying three-dimensional space, pheromone, and Terrain. The Genetic algorithm implementation maintained and preserved the coding style, data structure, and visualization from the PSO implementation. Although the Genetic Algorithm has higher fitness values and better global solutions for 3 or more receivers, it increases the running time. The Genetic Algorithm is around (15-30%) more accurate for asset counts from 3 to 6 but requires (26-82%) more computational time. When the allocation problem complexity increases by adding 3D space, pheromones and complex terrains, the accuracy of GA is 3.71% better but the speed of GA is 121% slower than PSO. In summary, the Genetic Algorithm gives a better global solution in some cases but the computational time is higher for the Genetic Algorithm with than Particle Swarm Optimization.</div>

Submitted by Geyciane Santos (geyciane_thamires@hotmail.com) on 2015-06-24T12:53:55Z No. of bitstreams: 1 Dissertação - Elda Nunes de Carvalho.pdf: 2461744 bytes, checksum: 32fc857bfdb9e7a48176274b174bf16e (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-06-24T15:01:36Z (GMT) No. of bitstreams: 1 Dissertação - Elda Nunes de Carvalho.pdf: 2461744 bytes, checksum: 32fc857bfdb9e7a48176274b174bf16e (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-06-24T15:01:43Z (GMT) No. of bitstreams: 1 Dissertação - Elda Nunes de Carvalho.pdf: 2461744 bytes, checksum: 32fc857bfdb9e7a48176274b174bf16e (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-06-24T15:19:31Z (GMT) No. of bitstreams: 1 Dissertação - Elda Nunes de Carvalho.pdf: 2461744 bytes, checksum: 32fc857bfdb9e7a48176274b174bf16e (MD5) / Made available in DSpace on 2015-06-24T15:19:31Z (GMT). No. of bitstreams: 1 Dissertação - Elda Nunes de Carvalho.pdf: 2461744 bytes, checksum: 32fc857bfdb9e7a48176274b174bf16e (MD5) Previous issue date: 2014-02-28 / Unmanned aerial vehicles (UAV’s) have been the target of research in many applications where there are hazardous conditions to the integrity of the crew. A great effort has been made to increase the autonomy of UAV’s to perform these missions. In the military domain these studies have shown good results. On the other hand, in civil area, researches are still a challenge, due to the lack of investments for the development and absence of specific rules. However, in some situations, the lack of pilots onboard becomes mission execution a nontrivial task. An example is missions on dense forest environments with, specific feature of the Amazon region. This scenario presents a critical role both in autonomous navigation mode and the remotely operated mode, due to the difficulty of navigation in such environments. The use of planners has enabled obtaining paths or trajectories that satisfy criteria navigation performance meeting the requirements of the mission. In general, a set of points is defined in the vehicle navigation space and the planner finds a path that passes through all of these points, taking into account the constraints of motion, time and / or energy consumption of the vehicle. This work addresses the construction of a path planner for fixed wing UAV navigation in three dimensional spaces about sparse forest regions with restriction of flight due to weather and environmental conditions. Unlike the traditional way, the methodology developed here gets specific points in three-dimensional space regions that favor the criteria for flight safety and maneuverability of the aircraft and also meet the requirements of the mission, paving the way from the interpolation of these points with points crossing the coming mission. Experimental tests by simulation in different scenarios show the applicability of UAVpath planner in actual service security and improving mission performance. / Os veículos aéreos não tripulados (VANT) têm sido alvo de pesquisas em diversasaplicações.Um grande esforço tem sido feito para aumentar a autonomia dos VANT’s ao realizar essas missões. Na área militar essas pesquisas têm mostrados bons resultados, por outro lado, na área civil as pesquisas aindasão um desafio, pela carência de investimentos para o desenvolvimento e falta de regulamentação especifica. No entanto, em algumas situações, a falta de pilotoabordo torna a execução da missão uma tarefa não trivial. Um exemplo disso são missões realizadas sobre ambientes florestais com mata densa, característica especificada Região Amazônica. Este cenárioapresentaum papel crítico tanto no modo de navegação autônoma quanto no modoremotamente operado, devido àdificuldade de navegação em tais ambientes. O uso de planejadores tem viabilizadoa obtenção de caminhos ou trajetórias que satisfaçam critérios de desempenho de navegação atendendo aos requisitos da missão. Em geral, um conjunto de pontos passagem é definido no espaço de navegação do veículo e o planejador encontra um caminho que passa por sobre todos esses pontos, levando em consideração as restrições de movimento, tempo, condições ambientaise/ou o consumo de energia do veiculo.Este trabalhoaborda a construção de um planejador de caminho para navegação do VANT de asa fixa no espaço tridimensional sobre regiões esparsas de florestascom restrição de voo devido àscondições de dirigibilidade, climáticas, ambientais. De maneira diferente das tradicionais, a metodologia aqui desenvolvida obtém pontos específicos em regiões do espaço tridimensional que favoreçam os critérios de segurança do voo e dirigibilidade da aeronave e também atendam aos requisitos da missão, construindo o caminho a partir da interpolação desses pontos com os pontos de passagem oriundos da missão. Testes experimentais por meio de simulaçãoem diferentes cenários mostram à aplicabilidade do planejador de caminho em VANT reais no atendimento a segurançae na melhoria do desempenho da missão.

Analyzing ranking data is an essential component in a wide range of important applications including web-search and recommendation systems. Rankings are difficult to visualize or model due to the computational difficulties associated with the large number of items. On the other hand, partial or incomplete rankings induce more difficulties since approaches that adapt well to typical types of rankings cannot apply generally to all types. While analyzing ranking data has a long history in statistics, construction of an efficient framework to analyze incomplete ranking data (with or without ties) is currently an open problem. This thesis addresses the problem of scalability for visualizing and modeling partial incomplete rankings. In particular, we propose a distance measure for top-k rankings with the following three properties: (1) metric, (2) emphasis on top ranks, and (3) computational efficiency. Given the distance measure, the data can be projected into a low dimensional continuous vector space via multi-dimensional scaling (MDS) for easy visualization. We further propose a non-parametric model for estimating distributions of partial incomplete rankings. For the non-parametric estimator, we use a triangular kernel that is a direct analogue of the Euclidean triangular kernel. The computational difficulties for large n are simplified using combinatorial properties and generating functions associated with symmetric groups. We show that our estimator is computational efficient for rankings of arbitrary incompleteness and tie structure. Moreover, we propose an efficient learning algorithm to construct a preference elicitation system from partial incomplete rankings, which can be used to solve the cold-start problems in ranking recommendations. The proposed approaches are examined in experiments with real search engine and movie recommendation data.

Harmony search method based optimum design algorithm is presented for the grillage systems. This numerical optimization technique imitates the musical performance process that takes place when a musician searches for a better state of harmony. For instance, jazz improvisation seeks to find musically pleasing harmony similar to the optimum design process which seeks to find the optimum solution. The design algorithm considers the displacement and strength constraints which are implemented from LRFD-AISC (Load and Resistance Factor Design-American Institute of Steel Construction). It selects the appropriate W (Wide Flange)-sections for the transverse and longitudinal beams of the grillage system among 272 discrete W-section designations given in LRFD-AISC so that the design limitations described in LRFD are satisfied and the weight of the system is confined to be minimal. Number of design examples is considered to demonstrate the efficiency of the algorithm presented.

Harmony search method based optimum topology design algorithm is presented for single layer lamella domes. The harmony search method is a numerical optimization technique developed recently that imitates the musical performance process which takes place when a musician searches for a better state of harmony. Jazz improvisation seeks to find musically pleasing harmony similar to the optimum design process which seeks to find the optimum solution. The optimum design algorithm developed imposes the behavioral and performance constraints in accordance with LRFD-AISC. The optimum number of rings, the height of the crown and the tubular cross-sectional designations for dome members are treated as design variables. The member grouping is allowed so that the same section can be adopted for each group. The design algorithm developed has a routine that build the data for the geometry of the dome automatically that covers the numbering of joints, and member incidences, and the computation of the coordinates of joints. Due to the slenderness and the presence of imperfections in dome structures it is necessary to consider the geometric nonlinearity in the prediction of their response under the external loading. Design examples are considered to demonstrate the efficiency of the algorithm presented.