Three Facets of Online Political Networks: Communities, Antagonisms, and Polarization

abstract: Millions of users leave digital traces of their political engagements on social media platforms every day. Users form networks of interactions, produce textual content, like and share each others' content. This creates an invaluable opportunity to better understand the political engagements of internet users. In this proposal, I present three algorithmic solutions to three facets of online political networks; namely, detection of communities, antagonisms and the impact of certain types of accounts on political polarization. First, I develop a multi-view community detection algorithm to find politically pure communities. I find that word usage among other content types (i.e. hashtags, URLs) complement user interactions the best in accurately detecting communities. Second, I focus on detecting negative linkages between politically motivated social media users. Major social media platforms do not facilitate their users with built-in negative interaction options. However, many political network analysis tasks rely on not only positive but also negative linkages. Here, I present the SocLSFact framework to detect negative linkages among social media users. It utilizes three pieces of information; sentiment cues of textual interactions, positive interactions, and socially balanced triads. I evaluate the contribution of each three aspects in negative link detection performance on multiple tasks. Third, I propose an experimental setup that quantifies the polarization impact of automated accounts on Twitter retweet networks. I focus on a dataset of tragic Parkland shooting event and its aftermath. I show that when automated accounts are removed from the retweet network the network polarization decrease significantly, while a same number of accounts to the automated accounts are removed randomly the difference is not significant. I also find that prominent predictors of engagement of automatically generated content is not very different than what previous studies point out in general engaging content on social media. Last but not least, I identify accounts which self-disclose their automated nature in their profile by using expressions such as bot, chat-bot, or robot. I find that human engagement to self-disclosing accounts compared to non-disclosing automated accounts is much smaller. This observational finding can motivate further efforts into automated account detection research to prevent their unintended impact.Dissertation/ThesisDoctoral Dissertation Computer Science 201

A Competent Convolutional Sparse Representation Model for Pan-Sharpening of Multi-Spectral Images

Two types of images are produced by Earth observation satellites, each having complementing spatial andspectral characteristics. Pan-sharpening (PS) is based on remote sensing and image fusion approach thatproduces a high spatial resolution multi-spectral image by merging spectral information from a low spatialresolution multispectral (MS) image with intrinsic spatial details from a high spatial resolution panchromatic(PAN) image. Traditional pan-sharpening methods continue to seek for a fused image that contains thenecessary spatial and spectral information. This work proposes a pan-sharpening method based on a recentinvention, convolutional sparse representation (CSR). Geometric structural characteristics are extracted fromthe PAN image using a CSR-based filtering procedure. The challenge of learning filters, convolutional basispursuit denoising (CBPDN), is handled using a modified dictionary learning method based on the concept ofAlternating Direction Method of Multipliers (ADMM). The retrieved details are put into MS bands usingapplicable weighting coefficients. Because the proposed fusion model avoids the standard patch-basedmethod, spatial and structural features are preserved while spectral quality is maintained. The spectraldistortion index SAM and the spatial measure ERGAS improve by 4.4 and 6.2 percent, respectively, whencompared to SR-based techniques. The computational complexity is reduced by 200 seconds when compared to the most recent SR-based fusion technique. The proposed method's efficacy is demonstrated by reduced-scale and full-scale experimental findings utilising the QuickBird and GeoEye-1 datasets

Personalized learning paths based on Wikipedia article statistics

We propose a new semi-automated method for generating personalized learning paths from the Wikipediaonline encyclopedia by following inter-article hyperlink chains based on various rankings that are retrieved from the statistics of the articles. Alternative perspectives for learning topics are achieved when the next hyperlink to access is selected based on hierarchy of hyperlinks, repetition of hyperlink terms, article size, viewing rate, editing rate, or user-defined weighted mixture of them all. We have implemented the method in a prototype enabling the learner to build independently concept maps following her needs and consideration. A list of related concepts is shown in a desired type of ranking to label new nodes (titles of target articles for current hyperlinks) accompanied with parsed explanation phrases from the sentences surrounding each hyperlink to label directed arcs connecting nodes. In experiments the alternative ranking schemes well supported various learning needs suggesting new pedagogical networking practices.Peer reviewe

Advancing Perception in Artificial Intelligence through Principles of Cognitive Science

Although artificial intelligence (AI) has achieved many feats at a rapid pace, there still exist open problems and fundamental shortcomings related to performance and resource efficiency. Since AI researchers benchmark a significant proportion of performance standards through human intelligence, cognitive sciences-inspired AI is a promising domain of research. Studying cognitive science can provide a fresh perspective to building fundamental blocks in AI research, which can lead to improved performance and efficiency. In this review paper, we focus on the cognitive functions of perception, which is the process of taking signals from one's surroundings as input, and processing them to understand the environment. Particularly, we study and compare its various processes through the lens of both cognitive sciences and AI. Through this study, we review all current major theories from various sub-disciplines of cognitive science (specifically neuroscience, psychology and linguistics), and draw parallels with theories and techniques from current practices in AI. We, hence, present a detailed collection of methods in AI for researchers to build AI systems inspired by cognitive science. Further, through the process of reviewing the state of cognitive-inspired AI, we point out many gaps in the current state of AI (with respect to the performance of the human brain), and hence present potential directions for researchers to develop better perception systems in AI.Comment: Summary: a detailed review of the current state of perception models through the lens of cognitive A

Meaning versus Grammar

This volume investigates the complicated relationship between grammar, computation, and meaning in natural languages. It details conditions under which meaning-driven processing of natural language is feasible, discusses an operational and accessible implementation of the grammatical cycle for Dutch, and offers analyses of a number of further conjectures about constituency and entailment in natural language

Efficient machine learning: models and accelerations

One of the key enablers of the recent unprecedented success of machine learning is the adoption of very large models. Modern machine learning models typically consist of multiple cascaded layers such as deep neural networks, and at least millions to hundreds of millions of parameters (i.e., weights) for the entire model. The larger-scale model tend to enable the extraction of more complex high-level features, and therefore, lead to a significant improvement of the overall accuracy. On the other side, the layered deep structure and large model sizes also demand to increase computational capability and memory requirements. In order to achieve higher scalability, performance, and energy efficiency for deep learning systems, two orthogonal research and development trends have attracted enormous interests. The first trend is the acceleration while the second is the model compression. The underlying goal of these two trends is the high quality of the models to provides accurate predictions. In this thesis, we address these two problems and utilize different computing paradigms to solve real-life deep learning problems. To explore in these two domains, this thesis first presents the cogent confabulation network for sentence completion problem. We use Chinese language as a case study to describe our exploration of the cogent confabulation based text recognition models. The exploration and optimization of the cogent confabulation based models have been conducted through various comparisons. The optimized network offered a better accuracy performance for the sentence completion. To accelerate the sentence completion problem in a multi-processing system, we propose a parallel framework for the confabulation recall algorithm. The parallel implementation reduce runtime, improve the recall accuracy by breaking the fixed evaluation order and introducing more generalization, and maintain a balanced progress in status update among all neurons. A lexicon scheduling algorithm is presented to further improve the model performance. As deep neural networks have been proven effective to solve many real-life applications, and they are deployed on low-power devices, we then investigated the acceleration for the neural network inference using a hardware-friendly computing paradigm, stochastic computing. It is an approximate computing paradigm which requires small hardware footprint and achieves high energy efficiency. Applying this stochastic computing to deep convolutional neural networks, we design the functional hardware blocks and optimize them jointly to minimize the accuracy loss due to the approximation. The synthesis results show that the proposed design achieves the remarkable low hardware cost and power/energy consumption. Modern neural networks usually imply a huge amount of parameters which cannot be fit into embedded devices. Compression of the deep learning models together with acceleration attracts our attention. We introduce the structured matrices based neural network to address this problem. Circulant matrix is one of the structured matrices, where a matrix can be represented using a single vector, so that the matrix is compressed. We further investigate a more flexible structure based on circulant matrix, called block-circulant matrix. It partitions a matrix into several smaller blocks and makes each submatrix is circulant. The compression ratio is controllable. With the help of Fourier Transform based equivalent computation, the inference of the deep neural network can be accelerated energy efficiently on the FPGAs. We also offer the optimization for the training algorithm for block circulant matrices based neural networks to obtain a high accuracy after compression

Leveraging Machine Learning to Explain the Nature of Written Genres

The analysis of discourse and the study of what characterizes it in terms of communicative objectives is essential to most tasks of Natural Language Processing. Consequently, research on textual genres as expressions of such objectives presents an opportunity to enhance both automatic techniques and resources. To conduct an investigation of this kind, it is necessary to have a good understanding of what defines and distinguishes each textual genre. This research presents a data-driven approach to discover and analyze patterns in several textual genres with the aim of identifying and quantifying the differences between them, considering how language is employed and meaning expressed in each particular case. To identify and analyze patterns within genres, a set of linguistic features is first defined, extracted and computed by using several Natural Language Processing tools. Specifically, the analysis is performed over a corpora of documents—containing news, tales and reviews—gathered from different sources to ensure an heterogeneous representation. Once the feature dataset has been generated, machine learning techniques are used to ascertain how and to what extent each of the features should be present in a document depending on its genre. The results show that the set of features defined is relevant for characterizing the different genres. Furthermore, the findings allow us to perform a qualitative analysis of such features, so that their usefulness and suitability is corroborated. The results of the research can benefit natural language discourse processing tasks, which are useful both for understanding and generating language.This work was supported in part by the Ministry of Science and Innovation of Spain for the project “Integer: Intelligent Text Generarion” under Grant RTI2018-094649-B-I00, and in part by the Generalitat Valenciana through project “SIIA: Tecnologias del lenguaje humano para una sociedad inclusiva, igualitaria, y accesible" under Grant PROMETEU/2018/089