NetPanorama: A Declarative Grammar for Network Construction, Transformation, and Visualization

This paper introduces NetPanorama, a domain-specific language and declarative grammar for interactive network visualizations. Exploring complex networks with multivariate, geographical, or temporal information often require bespoke visualization designs, such as adjacency matrices, arc-diagrams, small multiples, timelines, or geographic map visualizations. However, creating these requires implementing data loading, data transformations, visualization, and interactivity, which is time-consuming and slows down the iterative exploration of this huge design space. With NetPanorama, a developer specifies a network visualization design as a pipeline of parameterizable steps. Our specification and reference implementation aims to facilitate visualization development and reuse; allow for easy design exploration and iteration; and make data transformation and visual mapping decisions transparent. Documentation, source code, examples, and an interactive online editor can be found online: https://netpanorama.netlify.app

Análise visual do processo de espalhamento de doenças infecciosas por meio de redes temporais

Trabalho de Conclusão de Curso (Graduação)As redes temporais são uma maneira útil de representar instâncias de dados e suas interações ao longo do tempo. As características dessas redes, tais como: alta concentração de interações e ociosidade, as tornam adequadas para a simulação e análise de processos dinâmicos, como por exemplo a transmissão de doenças infecciosas. Este trabalho visa simular e analisar, por meio de estratégias de visualização de redes temporais, diferentes cenários de espalhamento de doenças infecciosas, com intuito de ressaltar sua utilização neste contexto. Além disso, possibilita órgãos governamentais, bem como instituições de pesquisa uma melhor análise diante de um cenário epidêmico e, consequentemente o auxílio na tomada de decisões. Por meio de dois estudos de caso com redes temporais reais coletadas a partir de ambientes escolares, concluímos que, além de demonstrarem o potencial de estratégias de visualização para esse tipo de estudo, corroboram com estudos que defendem a alta eĄciência de grandes níveis de isolamento social e de medidas preventivas pessoais, como o uso de equipamentos de proteção individual

Towards Interaction-level Video Action Understanding

A huge amount of videos have been created, spread, and viewed daily. Among these massive videos, the actions and activities of humans account for a large part. We desire machines to understand human actions in videos as this is essential to various applications, including but not limited to autonomous driving cars, security systems, human-robot interactions and healthcare. Towards real intelligent system that is able to interact with humans, video understanding must go beyond simply answering ``what is the action in the video", but be more aware of what those actions mean to humans and be more in line with human thinking, which we call interactive-level action understanding. This thesis identifies three main challenges to approaching interactive-level video action understanding: 1) understanding actions given human consensus; 2) understanding actions based on specific human rules; 3) directly understanding actions in videos via human natural language. For the first challenge, we select video summary as a representative task that aims to select informative frames to retain high-level information based on human annotators' experience. Through self-attention architecture and meta-learning, which jointly process dual representations of visual and sequential information for video summarization, the proposed model is capable of understanding video from human consensus (e.g., how humans think which parts of an action sequence are essential). For the second challenge, our works on action quality assessment utilize transformer decoders to parse the input action into several sub-actions and assess the more fine-grained qualities of the given action, yielding the capability of action understanding given specific human rules. (e.g., how well a diving action performs, how well a robot performs surgery) The third key idea explored in this thesis is to use graph neural networks in an adversarial fashion to understand actions through natural language. We demonstrate the utility of this technique for the video captioning task, which takes an action video as input, outputs natural language, and yields state-of-the-art performance. It can be concluded that the research directions and methods introduced in this thesis provide fundamental components toward interactive-level action understanding

Proceedings of the 18th Biennial Conference of the Palaeontological Society of Southern Africa Johannesburg, 11–14 July 2014

Evolutionary Studies Institute the Palaeontological Scientific Trust (PAST) and its Scatterlings of Africa programmes University of the Witwatersrand Department of Science and Technology National Research Foundation Centre of Excellence, Palaeoscience

Analyse et détection des trajectoires d'approches atypiques des aéronefs à l'aide de l'analyse de données fonctionnelles et de l'apprentissage automatique

L'amélioration de la sécurité aérienne implique généralement l'identification, la détection et la gestion des événements indésirables qui peuvent conduire à des événements finaux mortels. De précédentes études menées par la DSAC, l'autorité de surveillance française, ont permis d'identifier les approches non-conformes présentant des déviations par rapport aux procédures standards comme des événements indésirables. Cette thèse vise à explorer les techniques de l'analyse de données fonctionnelles et d'apprentissage automatique afin de fournir des algorithmes permettant la détection et l'analyse de trajectoires atypiques en approche à partir de données sol. Quatre axes de recherche sont abordés. Le premier axe vise à développer un algorithme d'analyse post-opérationnel basé sur des techniques d'analyse de données fonctionnelles et d'apprentissage non-supervisé pour la détection de comportements atypiques en approche. Le modèle sera confronté à l'analyse des bureaux de sécurité des vols des compagnies aériennes, et sera appliqué dans le contexte particulier de la période COVID-19 pour illustrer son utilisation potentielle alors que le système global ATM est confronté à une crise. Le deuxième axe de recherche s'intéresse plus particulièrement à la génération et à l'extraction d'informations à partir de données radar à l'aide de nouvelles techniques telles que l'apprentissage automatique. Ces méthodologies permettent d'améliorer la compréhension et l'analyse des trajectoires, par exemple dans le cas de l'estimation des paramètres embarqués à partir des paramètres radar. Le troisième axe, propose de nouvelles techniques de manipulation et de génération de données en utilisant le cadre de l'analyse de données fonctionnelles. Enfin, le quatrième axe se concentre sur l'extension en temps réel de l'algorithme post-opérationnel grâce à l'utilisation de techniques de contrôle optimal, donnant des pistes vers de nouveaux systèmes d'alerte permettant une meilleure conscience de la situation.Improving aviation safety generally involves identifying, detecting and managing undesirable events that can lead to final events with fatalities. Previous studies conducted by the French National Supervisory Authority have led to the identification of non-compliant approaches presenting deviation from standard procedures as undesirable events. This thesis aims to explore functional data analysis and machine learning techniques in order to provide algorithms for the detection and analysis of atypical trajectories in approach from ground side. Four research directions are being investigated. The first axis aims to develop a post-op analysis algorithm based on functional data analysis techniques and unsupervised learning for the detection of atypical behaviours in approach. The model is confronted with the analysis of airline flight safety offices, and is applied in the particular context of the COVID-19 crisis to illustrate its potential use while the global ATM system is facing a standstill. The second axis of research addresses the generation and extraction of information from radar data using new techniques such as Machine Learning. These methodologies allow to \mbox{improve} the understanding and the analysis of trajectories, for example in the case of the estimation of on-board parameters from radar parameters. The third axis proposes novel data manipulation and generation techniques using the functional data analysis framework. Finally, the fourth axis focuses on extending the post-operational algorithm into real time with the use of optimal control techniques, giving directions to new situation awareness alerting systems

INERTIAL MOTION CAPTURE SYSTEM FOR BIOMECHANICAL ANALYSIS IN PRESSURE SUITS

A non-invasive system has been developed at the University of Maryland Space System Laboratory with the goal of providing a new capability for quantifying the motion of the human inside a space suit. Based on an array of six microprocessors and eighteen microelectromechanical (MEMS) inertial measurement units (IMUs), the Body Pose Measurement System (BPMS) allows the monitoring of the kinematics of the suit occupant in an unobtrusive, self-contained, lightweight and compact fashion, without requiring any external equipment such as those necessary with modern optical motion capture systems. BPMS measures and stores the accelerations, angular rates and magnetic fields acting upon each IMU, which are mounted on the head, torso, and each segment of each limb. In order to convert the raw data into a more useful form, such as a set of body segment angles quantifying pose and motion, a series of geometrical models and a non-linear complimentary filter were implemented. The first portion of this works focuses on assessing system performance, which was measured by comparing the BPMS filtered data against rigid body angles measured through an external VICON optical motion capture system. This type of system is the industry standard, and is used here for independent measurement of body pose angles. By comparing the two sets of data, performance metrics such as BPMS system operational conditions, accuracy, and drift were evaluated and correlated against VICON data. After the system and models were verified and their capabilities and limitations assessed, a series of pressure suit evaluations were conducted. Three different pressure suits were used to identify the relationship between usable range of motion and internal suit pressure. In addition to addressing range of motion, a series of exploration tasks were also performed, recorded, and analysed in order to identify different motion patterns and trajectories as suit pressure is increased and overall suit mobility is reduced. The focus of these evaluations was to quantify the reduction in mobility when operating in any of the evaluated pressure suits. This data should be of value in defining new low cost alternatives for pressure suit performance verification and evaluation. This work demonstrates that the BPMS technology is a viable alternative or companion to optical motion capture; while BPMS is the first motion capture system that has been designed specifically to measure the kinematics of a human in a pressure suit, its capabilities are not constrained to just being a measurement tool. The last section of the manuscript is devoted to future possible uses for the system, with a specific focus on pressure suit applications such in the use of BPMS as a master control interface for robot teleoperation, as well as an input interface for future robotically augmented pressure suits

Telecommunication Systems

This book is based on both industrial and academic research efforts in which a number of recent advancements and rare insights into telecommunication systems are well presented. The volume is organized into four parts: "Telecommunication Protocol, Optimization, and Security Frameworks", "Next-Generation Optical Access Technologies", "Convergence of Wireless-Optical Networks" and "Advanced Relay and Antenna Systems for Smart Networks." Chapters within these parts are self-contained and cross-referenced to facilitate further study

Binary RDF for Scalable Publishing, Exchanging and Consumption in the Web of Data

El actual diluvio de datos está inundando la web con grandes volúmenes de datos representados en RDF, dando lugar a la denominada 'Web de Datos'. En esta tesis proponemos, en primer lugar, un estudio profundo de aquellos textos que nos permitan abordar un conocimiento global de la estructura real de los conjuntos de datos RDF, HDT, que afronta la representación eficiente de grandes volúmenes de datos RDF a través de estructuras optimizadas para su almacenamiento y transmisión en red. HDT representa efizcamente un conjunto de datos RDF a través de su división en tres componentes: la cabecera (Header), el diccionario (Dictionary) y la estructura de sentencias RDF (Triples). A continuación, nos centramos en proveer estructuras eficientes de dichos componentes, ocupando un espacio comprimido al tiempo que se permite el acceso directo a cualquier dat