8,568 research outputs found
Recommended from our members
Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Technical Dimensions of Programming Systems
Programming requires much more than just writing code in a programming language. It is usually done in the context of a stateful environment, by interacting with a system through a graphical user interface. Yet, this wide space of possibilities lacks a common structure for navigation. Work on programming systems fails to form a coherent body of research, making it hard to improve on past work and advance the state of the art.
In computer science, much has been said and done to allow comparison of programming languages, yet no similar theory exists for programming systems; we believe that programming systems deserve a theory too.
We present a framework of technical dimensions which capture the underlying characteristics of programming systems and provide a means for conceptualizing and comparing them.
We identify technical dimensions by examining past influential programming systems and reviewing their design principles, technical capabilities, and styles of user interaction. Technical dimensions capture characteristics that may be studied, compared and advanced independently. This makes it possible to talk about programming systems in a way that can be shared and constructively debated rather than relying solely on personal impressions.
Our framework is derived using a qualitative analysis of past programming systems. We outline two concrete ways of using our framework. First, we show how it can analyze a recently developed novel programming system. Then, we use it to identify an interesting unexplored point in the design space of programming systems.
Much research effort focuses on building programming systems that are easier to use, accessible to non-experts, moldable and/or powerful, but such efforts are disconnected. They are informal, guided by the personal vision of their authors and thus are only evaluable and comparable on the basis of individual experience using them. By providing foundations for more systematic research, we can help programming systems researchers to stand, at last, on the shoulders of giants
Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes
Humans have long been recorded in a variety of forms since antiquity. For
example, sculptures and paintings were the primary media for depicting human
beings before the invention of cameras. However, most current human-centric
computer vision tasks like human pose estimation and human image generation
focus exclusively on natural images in the real world. Artificial humans, such
as those in sculptures, paintings, and cartoons, are commonly neglected, making
existing models fail in these scenarios. As an abstraction of life, art
incorporates humans in both natural and artificial scenes. We take advantage of
it and introduce the Human-Art dataset to bridge related tasks in natural and
artificial scenarios. Specifically, Human-Art contains 50k high-quality images
with over 123k person instances from 5 natural and 15 artificial scenarios,
which are annotated with bounding boxes, keypoints, self-contact points, and
text information for humans represented in both 2D and 3D. It is, therefore,
comprehensive and versatile for various downstream tasks. We also provide a
rich set of baseline results and detailed analyses for related tasks, including
human detection, 2D and 3D human pose estimation, image generation, and motion
transfer. As a challenging dataset, we hope Human-Art can provide insights for
relevant research and open up new research questions.Comment: CVPR202
Single Image Depth Prediction Made Better: A Multivariate Gaussian Take
Neural-network-based single image depth prediction (SIDP) is a challenging
task where the goal is to predict the scene's per-pixel depth at test time.
Since the problem, by definition, is ill-posed, the fundamental goal is to come
up with an approach that can reliably model the scene depth from a set of
training examples. In the pursuit of perfect depth estimation, most existing
state-of-the-art learning techniques predict a single scalar depth value
per-pixel. Yet, it is well-known that the trained model has accuracy limits and
can predict imprecise depth. Therefore, an SIDP approach must be mindful of the
expected depth variations in the model's prediction at test time. Accordingly,
we introduce an approach that performs continuous modeling of per-pixel depth,
where we can predict and reason about the per-pixel depth and its distribution.
To this end, we model per-pixel scene depth using a multivariate Gaussian
distribution. Moreover, contrary to the existing uncertainty modeling methods
-- in the same spirit, where per-pixel depth is assumed to be independent, we
introduce per-pixel covariance modeling that encodes its depth dependency w.r.t
all the scene points. Unfortunately, per-pixel depth covariance modeling leads
to a computationally expensive continuous loss function, which we solve
efficiently using the learned low-rank approximation of the overall covariance
matrix. Notably, when tested on benchmark datasets such as KITTI, NYU, and
SUN-RGB-D, the SIDP model obtained by optimizing our loss function shows
state-of-the-art results. Our method's accuracy (named MG) is among the top on
the KITTI depth-prediction benchmark leaderboard.Comment: Accepted to IEEE/CVF CVPR 2023. Draft info: 17 pages, 13 Figures, 9
Table
Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends
Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow estimation has been an interest of research in computer vision and computer graphics for a long time. It is also a fundamental task for various applications such as autonomous driving. Compared to previous methods that utilize image representations, many recent researches build upon the power of deep analysis and focus on point clouds representation to conduct 3D flow estimation. This paper comprehensively reviews the pioneering literature in scene flow estimation based on point clouds. Meanwhile, it delves into detail in learning paradigms and presents insightful comparisons between the state-of-the-art methods using deep learning for scene flow estimation. Furthermore, this paper investigates various higher-level scene understanding tasks, including object tracking, motion segmentation, etc. and concludes with an overview of foreseeable research trends for scene flow estimation
Self-Supervised Scene Dynamic Recovery from Rolling Shutter Images and Events
Scene Dynamic Recovery (SDR) by inverting distorted Rolling Shutter (RS)
images to an undistorted high frame-rate Global Shutter (GS) video is a
severely ill-posed problem, particularly when prior knowledge about
camera/object motions is unavailable. Commonly used artificial assumptions on
motion linearity and data-specific characteristics, regarding the temporal
dynamics information embedded in the RS scanlines, are prone to producing
sub-optimal solutions in real-world scenarios. To address this challenge, we
propose an event-based RS2GS framework within a self-supervised learning
paradigm that leverages the extremely high temporal resolution of event cameras
to provide accurate inter/intra-frame information. % In this paper, we propose
to leverage the event camera to provide inter/intra-frame information as the
emitted events have an extremely high temporal resolution and learn an
event-based RS2GS network within a self-supervised learning framework, where
real-world events and RS images can be exploited to alleviate the performance
degradation caused by the domain gap between the synthesized and real data.
Specifically, an Event-based Inter/intra-frame Compensator (E-IC) is proposed
to predict the per-pixel dynamic between arbitrary time intervals, including
the temporal transition and spatial translation. Exploring connections in terms
of RS-RS, RS-GS, and GS-RS, we explicitly formulate mutual constraints with the
proposed E-IC, resulting in supervisions without ground-truth GS images.
Extensive evaluations over synthetic and real datasets demonstrate that the
proposed method achieves state-of-the-art and shows remarkable performance for
event-based RS2GS inversion in real-world scenarios. The dataset and code are
available at https://w3un.github.io/selfunroll/
The development of the Kent coalfield 1896-1946
One of the unique features of the Kent Coalfield is that it is entirely concealed by newer rocks. The existence of a coalfield under southern England, being a direct link between those of South Wales, Somerset and Bristol in the west and the Ruhr, Belgium. and northern France in the east, was predicted by the geologist R. A. C. Godwin-Austen as early as 1856. It was, however, only the rapid increase in demand for Britain's coal in the last quarter of the nineteenth century that made it worth considering testing this hypothesis. The first boring was made in the years 1886-90, and although it discovered coal, this did not in itself prove the existence of a viable coalfield. This could be done only by incurring the heavy cost of boring systematically over a wide area. As the financial returns from such an undertaking were uncertain, it was not surprising that in the early years, around the turn of the century, a dominant role was played by speculators, who were able to induce numerous small investors to risk some of their savings in the expectation of high profits. As minerals in Britain were privately owned, the early pioneer companies not only had to meet the cost of the exploratory borines, but also, if they were not to see the benefit of their work accrue to others, lease beforehand the right to mine coal from local landowners in as much of the surrounding area as possible. This policy was pursued most vigorously by Arthur Burr, a Surrey land specula tor, who raised capital by creating the Kent Coal Conoessions Ltd. and then floating a series of companies allied to it. Burr's enterprise would probably have been. successful had it not been for the water problems encountered at depth in -v- the coalfield. As a result, the Concessions group found itself in control of most of the coalfield, but without the necessary capital to sink and adequately equip its 01ffi collieries. By 1910, however, the discovery of iron ore deposits in east Kent, coupled with the fact that Kent coal was excellent for coking purposes, began to attract the large steel firms of Bolckow, Vaughan Ltd. and Dorman, Long & Co. Ltd. in to the area. The First World War intervened, however, to delay their plans, and to provide an extended lease of life to the Concessions group, which, by the summer of 1914, was facing financial collapse. By the time Dorman, Lone & Co, in alliance with Weetman Pearson (Lord Cowdray), had acquired control over the greater part of the coalfield from the Concessions group, not only was the country's coal industry declining, but so was its steel industry, which suffered an even more severe rate of contraction during the inter-war years. As a result, Pearson and Dorman Long Ltd. was forced to concentrate just on coal production, and this in turn was hampered not only by the water problems, but also by labour shortages and the schemes introduced by the government in 1930 to restrict the country's coal output, in an attempt to maintain prices and revenue in the industry. Nevertheless, production did show a substantial increase between 1927 and 1935, after which it declined as miners left the coalfield to return to their former districts, where employment opportunities were improving in the late thirties. Supporting roles were played in the inter-war years by Richard Tilden Smith, a share underwriter turned industrialist with long standing interests in the coalfield, who acquired one of the Concessions group's two collieries, and by the Powell Duffryn Steam Coal Co. Ltd., which through subsidiary companies, took over the only colliery to be developed by a pioneer company outside the Concessions group. The impossibility of Kent coal, because of its nature, ever gaining more than token access to the more lucrative household market, and then the failure of the local steel industry to materialise meant that the -vi- companies had to develop alternative outlets for their growing outputs. Although nearness to industrial markets in the south-east of England did confer certain advantages were poor consolation for the hoped for developments of either the early pioneers or the later industrialists. Instead of the expected profits, the companies mostly incurred losses, and only the company acquired by Powell Duffryn ever paid a dividend to its shareholders in the years before nationalisation. From the point of view of the Kent miners, the shortage of labour in the coalfield, particularly in the years 1914-20 and 1927-35, was to an important extent responsible for their being amongst the highest paid in the industry. At the same time the more favourable employment opportunities prevailing in Kent compared with other mining districts enabled the Kent Nine Workers Association to develop into a well organised union, which on the whole was able to look after the interests of its members fairly successfully. Throughout the period 1896 to 1946 the Kent Coalfield existed very much at the margin of the British coal industry. Its failure to develop substantially along the lines envisaged by either the early pioneers or by the later industrialists meant that its importance in national terms always remained small
How to Use Haptic Technology in Interactive Digital Documentation of Heritage
Virtual restoration of the extinct heritage is a method of reconstruction of an already destroyed work in a virtual environment. It represents a way and an opportunity to reenter the remodeled simulated space interactively and experience its contemporary atmosphere and former author expression. In addition to visual and acoustic experiences, haptic technologies represent the potential for expanding sensory perception, which is not yet sufficiently used in the architectural sector. This study focuses on defunct and endangered works of interior architecture and industrial heritage, which were significant at the time of their inception and shaped the direction of the industry. Especially in the case of cultural and spiritual heritage, we focus on interiors, which, by their short-term nature, are neither objective nor physically documentable. Selected extinct works for which there was enough data or there was still the possibility of consultation with a living author were experimentally virtually reconstructed. Using haptic technologies, we have expanded the observer’s ability to interactively analyze space and its context through User Tracking of observers. The data obtained in this way continue to help the creators of the architecture set new starting points and limits for the current creation and design as well
Um modelo para suporte automatizado ao reconhecimento, extração, personalização e reconstrução de gráficos estáticos
Data charts are widely used in our daily lives, being present in regular media,
such as newspapers, magazines, web pages, books, and many others. A well constructed
data chart leads to an intuitive understanding of its underlying data
and in the same way, when data charts have wrong design choices, a redesign
of these representations might be needed. However, in most cases, these
charts are shown as a static image, which means that the original data are not
usually available. Therefore, automatic methods could be applied to extract the
underlying data from the chart images to allow these changes. The task of
recognizing charts and extracting data from them is complex, largely due to the
variety of chart types and their visual characteristics.
Computer Vision techniques for image classification and object detection are
widely used for the problem of recognizing charts, but only in images without
any disturbance. Other features in real-world images that can make this task
difficult are not present in most literature works, like photo distortions, noise,
alignment, etc. Two computer vision techniques that can assist this task and
have been little explored in this context are perspective detection and
correction. These methods transform a distorted and noisy chart in a clear
chart, with its type ready for data extraction or other uses. The task of
reconstructing data is straightforward, as long the data is available the
visualization can be reconstructed, but the scenario of reconstructing it on the
same context is complex.
Using a Visualization Grammar for this scenario is a key component, as these
grammars usually have extensions for interaction, chart layers, and multiple
views without requiring extra development effort.
This work presents a model for automated support for custom recognition, and
reconstruction of charts in images. The model automatically performs the
process steps, such as reverse engineering, turning a static chart back into its
data table for later reconstruction, while allowing the user to make modifications
in case of uncertainties. This work also features a model-based architecture
along with prototypes for various use cases. Validation is performed step by
step, with methods inspired by the literature. This work features three use
cases providing proof of concept and validation of the model.
The first use case features usage of chart recognition methods focused on
documents in the real-world, the second use case focus on vocalization of
charts, using a visualization grammar to reconstruct a chart in audio format,
and the third use case presents an Augmented Reality application that
recognizes and reconstructs charts in the same context (a piece of paper)
overlaying the new chart and interaction widgets. The results showed that with
slight changes, chart recognition and reconstruction methods are now ready for
real-world charts, when taking time, accuracy and precision into consideration.Os gráficos de dados são amplamente utilizados na nossa vida diária, estando
presentes nos meios de comunicação regulares, tais como jornais, revistas,
páginas web, livros, e muitos outros. Um gráfico bem construído leva a uma
compreensão intuitiva dos seus dados inerentes e da mesma forma, quando
os gráficos de dados têm escolhas de conceção erradas, poderá ser
necessário um redesenho destas representações. Contudo, na maioria dos
casos, estes gráficos são mostrados como uma imagem estática, o que
significa que os dados originais não estão normalmente disponíveis. Portanto,
poderiam ser aplicados métodos automáticos para extrair os dados inerentes
das imagens dos gráficos, a fim de permitir estas alterações. A tarefa de
reconhecer os gráficos e extrair dados dos mesmos é complexa, em grande
parte devido à variedade de tipos de gráficos e às suas características visuais.
As técnicas de Visão Computacional para classificação de imagens e deteção
de objetos são amplamente utilizadas para o problema de reconhecimento de
gráficos, mas apenas em imagens sem qualquer ruído. Outras características
das imagens do mundo real que podem dificultar esta tarefa não estão
presentes na maioria das obras literárias, como distorções fotográficas, ruído,
alinhamento, etc. Duas técnicas de visão computacional que podem ajudar
nesta tarefa e que têm sido pouco exploradas neste contexto são a deteção e
correção da perspetiva. Estes métodos transformam um gráfico distorcido e
ruidoso em um gráfico limpo, com o seu tipo pronto para extração de dados
ou outras utilizações. A tarefa de reconstrução de dados é simples, desde que
os dados estejam disponíveis a visualização pode ser reconstruída, mas o
cenário de reconstrução no mesmo contexto é complexo.
A utilização de uma Gramática de Visualização para este cenário é um
componente chave, uma vez que estas gramáticas têm normalmente
extensões para interação, camadas de gráficos, e visões múltiplas sem exigir
um esforço extra de desenvolvimento.
Este trabalho apresenta um modelo de suporte automatizado para o
reconhecimento personalizado, e reconstrução de gráficos em imagens
estáticas. O modelo executa automaticamente as etapas do processo, tais
como engenharia inversa, transformando um gráfico estático novamente na
sua tabela de dados para posterior reconstrução, ao mesmo tempo que
permite ao utilizador fazer modificações em caso de incertezas. Este trabalho
também apresenta uma arquitetura baseada em modelos, juntamente com
protótipos para vários casos de utilização. A validação é efetuada passo a
passo, com métodos inspirados na literatura. Este trabalho apresenta três
casos de uso, fornecendo prova de conceito e validação do modelo.
O primeiro caso de uso apresenta a utilização de métodos de reconhecimento
de gráficos focando em documentos no mundo real, o segundo caso de uso
centra-se na vocalização de gráficos, utilizando uma gramática de visualização
para reconstruir um gráfico em formato áudio, e o terceiro caso de uso
apresenta uma aplicação de Realidade Aumentada que reconhece e reconstrói
gráficos no mesmo contexto (um pedaço de papel) sobrepondo os novos
gráficos e widgets de interação. Os resultados mostraram que com pequenas
alterações, os métodos de reconhecimento e reconstrução dos gráficos estão
agora prontos para os gráficos do mundo real, tendo em consideração o
tempo, a acurácia e a precisão.Programa Doutoral em Engenharia Informátic
- …