Interactive visual exploration of a large spatio-temporal dataset: Reflections on a geovisualization mashup

Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the clataset, the specific tools used and the 'rnashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

Micro-database for sustainability (ESG) indicators developed at the Banco de España (2022)

En los últimos años, la preocupación por los temas sociales y medioambientales ha ido en aumento y, en consecuencia, la demanda de datos sobre sostenibilidad se ha incrementado exponencialmente. Por esta razón, se ha desarrollado en el Departamento de Estadística del Banco de España una base de microdatos sobre indicadores de sostenibilidad (ESG). Este documento presenta dos artículos que analizan el proceso desarrollado para capturar esta información, así como las numerosas limitaciones y dificultades encontradas a lo largo del camino de búsqueda de microdatos sobre sostenibilidad. Concretamente, los dos temas que tratan los artículos son: “Analysing climate change data gaps” (presentado en la 11th Biennial IFC Conference on “Post-pandemic landscape for central bank statistics” durante los días 25-27 de agosto de 2022 en la sesión 3.B “Environmental statistics”) “Creation of a structured sustainability database from company reports: A web application prototype for information retrieval and storage” (presentado en el IFC Bank of Italy workshop on “Data science in central banking” los días 14-17 de febrero de 2022 en la sesión 4.3 “Text Mining and ML utilized in Economic Research”) (Koblents and Morales (2022)) El primer artículo se centra en las numerosas limitaciones encontradas y logros conseguidos en el proceso de desarrollo de la base de microdatos sobre indicadores de sostenibilidad para sociedades no financieras. Tras analizar detalladamente los estándares actuales de información ESG, consultar a expertos en la materia, analizar las obligaciones regulatorias y llevar a cabo un ejercicio práctico de búsqueda de esta información, se seleccionó una lista de los 39 indicadores más relevantes para comenzar la búsqueda. Actualmente se han recopilado más de 15.000 datos correspondientes al período 2019-2020 utilizando una herramienta semiautomática de búsqueda de información desarrollada internamente (presentado en detalle en el segundo artículo). Durante el proyecto se identificaron numerosas dificultades tales como el uso de diferentes métricas al reportar los indicadores, falta de información y de soporte digital para la descarga, así como dificultades de comparabilidad y restricciones regulatorias. El segundo artículo se centra en la herramienta desarrollada para crear la base de microdatos presentada en el primer artículo. Esta aplicación web tiene como objetivo, mediante la extracción y almacenamiento semiautomático, obtener los indicadores de sostenibilidad de los estados no financieros anuales presentados por las sociedades no financieras españolas. El objetivo de la aplicación es facilitar a los usuarios el trabajo de búsqueda de indicadores de sostenibilidad en múltiples documentos y su almacenamiento en una base de datos estructurada. La herramienta desarrollada incorpora un conjunto de términos de búsqueda predefinidos para cada indicador que han sido seleccionados en base a conocimiento experto e inteligencia artificial en desarrollos posteriores. Para cada empresa e indicador, la herramienta sugiere los fragmentos de texto más relevantes al usuario, quien a su vez identifica el valor correcto del indicador y lo almacena en la base de datos utilizando la interfaz web de usuario. Esta herramienta ha sido creada por dos científicos de datos en tres meses, con el apoyo continuo de un equipo de expertos que ha contribuido a la definición de requisitos y propuestas de mejora, la recopilación de datos, así como la validación y prueba de la herramienta. A lo largo del artículo, se realiza una descripción del enfoque técnico y los principales módulos del prototipo implementado, incluyendo la extracción de texto, indexación y búsqueda, almacenamiento de datos y visualización

Temporal hybridity: Mixing live video footage with instant replay in real time

Copyright @ 2010 ACMIn this paper we explore the production of streaming media that involves live and recorded content. To examine this, we report on how the production practices and process are conducted through an empirical study of the production of live television, involving the use of live and non-live media under highly time critical conditions. In explaining how this process is managed both as an individual and collective activity, we develop the concept of temporal hybridity to explain the properties of these kinds of production system and show how temporally separated media are used, understood and coordinated. Our analysis is examined in the light of recent developments in computing technology and we present some design implications to support amateur video production.The research was partly made possible by a grant from the Swedish Governmental Agency for Innovation Systems to the Mobile Life VinnExcellence Center, in partnership with SonyEricsson, Ericsson, Microsoft Research, Nokia Research, TeliaSonera and the City of Stockholm

Analysis and improvement proposal on self-supervised deep learning

Self-supervised learning is an emerging deep learning paradigm that aims at removing the label-dependency problems suffered by most supervised learning algorithms. Instance discrimination algorithms have proved to be very successful as they have reduced the gap between supervised and self-supervised ones to less than 5%. While most instance discrimination approaches focus on contrasting two augmentations of the same image, Neighbour Contrastive Learning approaches aim to increase the generalization of deep networks by pulling together representations from different images (neighbours) that belong to the same semantical class. However, they are limited mainly by their low accuracy regarding the neighbour selection. They also suffer from reduced efficiency while using multiple neighbours. Instance discrimination algorithms have their own particularities in solving the learning problem, and combining different approaches, bringing in the best of algorithms, is very interesting. In this thesis, we propose a neighbour contrast learning method called Musketeer. This method introduces Self-attention operations to create single representations, defined as centroids, from the extracted neighbours. Directly contrasting these centroids increases the neighbour retrieval accuracy while avoiding any efficiency loss. Moreover, Musketeer combines its neighbour contrast objective with a feature redundancy reduction objective, forming a symbiosis that proves to be beneficial in the overall performance of the framework. Our proposed symbiotic approach consistently outperforms SoTA instance discrimination frameworks on popular image classification benchmarking datasets, namely, CIFAR-10, CIFAR-100 and ImageNet-100. Additionally, we build an analysis pipeline that further explores the quantitative and qualitative results, providing numerous insights into the explainability of instance discrimination approaches

Deformable meshes for shape recovery: models and applications

With the advance of scanning and imaging technology, more and more 3D objects become available. Among them, deformable objects have gained increasing interests. They include medical instances such as organs, a sequence of objects in motion, and objects of similar shapes where a meaningful correspondence can be established between each other. Thus, it requires tools to store, compare, and retrieve them. Many of these operations depend on successful shape recovery. Shape recovery is the task to retrieve an object from the environment where its geometry is hidden or implicitly known. As a simple and versatile tool, mesh is widely used in computer graphics for modelling and visualization. In particular, deformable meshes are meshes which can take the deformation of deformable objects. They extend the modelling ability of meshes. This dissertation focuses on using deformable meshes to approach the 3D shape recovery problem. Several models are presented to solve the challenges for shape recovery under different circumstances. When the object is hidden in an image, a PDE deformable model is designed to extract its surface shape. The algorithm uses a mesh representation so that it can model any non-smooth surface with an arbitrary precision compared to a parametric model. It is more computational efficient than a level-set approach. When the explicit geometry of the object is known but is hidden in a bank of shapes, we simplify the deformation of the model to a graph matching procedure through a hierarchical surface abstraction approach. The framework is used for shape matching and retrieval. This idea is further extended to retain the explicit geometry during the abstraction. A novel motion abstraction framework for deformable meshes is devised based on clustering of local transformations and is successfully applied to 3D motion compression

Training of Self-Study Spelling Strategies and their Effectiveness on Fourth Grade Students

The purpose of this study was to determine the effects of specific study strategy training as a method of improving self-study spelling strategies of average fourth graders. Forty-four fourth graders comprised the treatment and control groups. Two fifteen word banks were constructed to serve as pre and posttests. With no directions as to which study strategies to employ, students in both groups were asked to study word lists for 15 minutes. Following the allotted time, the groups were pretested. Group A then received ten minute treatments for five weeks. These treatments included activities which emphasized visual memory and the development of self-study spelling strategies. Following the treatment the groups were posttested in the same manner as pretested. At t-test of dependent means revealed a significant gain for both treatment and control groups. Further calculations, however, showed a greater gain was achieved by the treatment group. From the results it can be concluded that a program of training self-study spelling strategies can make a difference on the spelling study skills of fourth grade children