EditorArrow: An arrow-based model for editor-based programming

2012 ◽  
Vol 23 (2) ◽  
pp. 185-224 ◽  
Author(s):  
PETER ACHTEN ◽  
MARKO VAN EEKELEN ◽  
MAARTEN DE MOL ◽  
RINUS PLASMEIJER
Keyword(s):  
Web Application ◽  
Proof Assistant ◽  
Semantic Model ◽  
Formal Reasoning ◽  
Interactive Applications ◽  
Level Of Abstraction ◽  
Event Handling ◽  
Reference Implementation ◽  
High Level ◽  
Shared State

AbstractState-based interactive applications, whether they run on the desktop or as a web application, can be considered as collections of interconnected editors of structured values that allow users to manipulate data. This is the view that is advocated by the GEC and iData toolkits, which offer a high level of abstraction to programming desktop and web GUI applications respectively. Special features of these toolkits are that editors have shared, persistent state, and that they handle events individually. In this paper we cast these toolkits within the Arrow framework and present EditorArrow: a single, unified semantic model that defines shared state and event handling. We study the properties of EditorArrow, and of editors in particular. Furthermore, we present the definedness properties of the combinators. A reference implementation of the EditorArrow model is given with some small program examples. We discuss formal reasoning about the model using the proof assistant Sparkle. The availability of this tool has proved to be indispensable in this endeavor.

“Farewell to Unjustified Enrichment?” – A Common Law Response

2016 ◽  
Vol 20 (3) ◽  
pp. 326-337
Author(s):  
Steve Hedley
Keyword(s):  
Common Law ◽  
German Law ◽  
Unjust Enrichment ◽  
Level Of Abstraction ◽  
The Law ◽  
Unjustified Enrichment ◽  
High Level ◽  
Logical Process

In this article, Professor Steve Hedley offers a Common Law response to he recently published arguments of Professor Nils Jansen on the German law of unjustified enrichment (as to which, see Jansen, “Farewell to Unjustified Enrichment” (2016) 20 EdinLR 123). The author takes the view that Jansen's paper provided a welcome opportunity to reconsider not merely what unjust enrichment can logically be, but what it is for. He argues that unjust enrichment talk contributes little of value, and that the supposedly logical process of stating it at a high level of abstraction, and then seeking to deduce the law from that abstraction, merely distracts lawyers from the equities of the cases they consider.

A Tight Connection Among Wealth, Income, Sustainability, and Accounting in an Ultra-Simplified Setting

2017 ◽  
Author(s):  
Martin L. Weitzman
Keyword(s):  
Economic Theory ◽  
Quantitative Relationship ◽  
Theoretical Relationship ◽  
Level Of Abstraction ◽  
Tight Connection ◽  
Basic Concepts ◽  
High Level ◽  
Very High

In theory, and under some very strong assumptions, there exists a tight quantitative relationship among the following four fundamental economic concepts: (1) ‘wealth’; (2) ‘income’; (3) ‘sustainability’; (4) ‘accounting’. These four basic concepts are placed in quotation marks here because a necessary first step will be to carefully and rigorously define what exactly is meant by each. This chapter reviews what is known about this important fourfold quantitative relationship in an ultra-simplified setting. It identifies some basic applications of this simplified economic theory of wealth and income (and sustainability and accounting). While the contents of this chapter are expressed at a very high level of abstraction and require many restrictive assumptions, the fundamental fourfold relationship it sharply highlights should be useful for conceptualizing, at least in principle, what is ‘wealth’ and what is its theoretical relationship to ‘income’, ‘sustainability’, and ‘accounting’.

scholarly journals Interpretation of Swedish Sign Language Using Convolutional Neural Networks and Transfer Learning

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Gustaf Halvardsson ◽  
Johanna Peterson ◽  
César Soto-Valero ◽  
Benoit Baudry
Keyword(s):  
Neural Networks ◽  
Sign Language ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Web Application ◽  
Training Dataset ◽  
Motion Processing ◽  
Image Perception ◽  
Sign Languages ◽  
High Level

AbstractThe automatic interpretation of sign languages is a challenging task, as it requires the usage of high-level vision and high-level motion processing systems for providing accurate image perception. In this paper, we use Convolutional Neural Networks (CNNs) and transfer learning to make computers able to interpret signs of the Swedish Sign Language (SSL) hand alphabet. Our model consists of the implementation of a pre-trained InceptionV3 network, and the usage of the mini-batch gradient descent optimization algorithm. We rely on transfer learning during the pre-training of the model and its data. The final accuracy of the model, based on 8 study subjects and 9400 images, is 85%. Our results indicate that the usage of CNNs is a promising approach to interpret sign languages, and transfer learning can be used to achieve high testing accuracy despite using a small training dataset. Furthermore, we describe the implementation details of our model to interpret signs as a user-friendly web application.

Exploiting high-level coherence information to optimize distributed shared state

2003 ◽  
Vol 38 (10) ◽  
pp. 131-142 ◽  
Author(s):  
DeQing Chen ◽  
Chunqiang Tang ◽  
Brandon Sanders ◽  
Sandhya Dwarkadas ◽  
Michael L. Scott
Keyword(s):  
High Level ◽  
Shared State

Urban Region Function Mining Service Based on Social Media Text Analysis

2021 ◽  
Vol 31 (04) ◽  
pp. 563-586
Author(s):  
Yanchun Sun ◽  
Hang Yin ◽  
Jiu Wen ◽  
Zhiyu Sun
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
Human Activities ◽  
Web Application ◽  
Classification Model ◽  
Urban Region ◽  
Urban Regions ◽  
Social Media Text ◽  
Urban Function ◽  
High Level

Urban region functions are the types of potential activities in an urban region, such as residence, commerce, transportation, entertainment, etc. A service which mines urban region functions is of great value for various applications, including urban planning and transportation management, etc. Many studies have been carried out to dig out different regions’ functions, but few studies are based on social media text analysis. Considering that the semantic information embedded in social media texts is very useful to infer an urban region’s main functions, we design a service which extracts human activities using Sina Weibo ( www.weibo.com ; the largest microblog system in Chinese, similar to Twitter) with location information and further describes a region’s main functions with a function vector based on the human activities. First, we predefine a variety of human activities to get the related activities corresponding to each Weibo post using an urban function classification model. Second, urban regions’ function vectors are generated, with which we can easily do some high-level work such as similar place recommendation. At last, with the function vectors generated, we develop a Web application for urban region function querying. We also conduct a case study among the urban regions in Beijing, and the experiment results demonstrate the feasibility of our method.

Semantic Matching, Propagation and Transformation for Composition in Component-Based Systems

2012 ◽  
pp. 122-141
Author(s):  
Eric Bouillet ◽  
Mark Feblowitz ◽  
Zhen Liu ◽  
Anand Ranganathan ◽  
Anton Riabov
Keyword(s):  
Information Processing ◽  
Semantic Matching ◽  
Semantic Model ◽  
Semantic Metadata ◽  
Software Applications ◽  
Component Development ◽  
Key Features ◽  
High Level ◽  
Semantic Properties

Composition of software applications from component parts in response to high-level goals is a long-standing and highly challenging goal. We target the problem of composition in flow-based information processing systems and demonstrate how application composition and component development can be facilitated by the use of semantically described application metadata. The semantic metadata describe both the data flowing through each application and the processing performed in the associated application code. In this paper, we explore some of the key features of the semantic model, including the matching of outputs to input requirements, and the transformation and the propagation of semantic properties by components.

scholarly journals Research on the Detection Method of Implicit Self Symmetry in a High-Level Semantic Model

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 28 ◽  
Author(s):  
Chao Wang
Keyword(s):  
Point Cloud ◽  
Detection Method ◽  
Cloud Model ◽  
High Reliability ◽  
Semantic Model ◽  
Symmetry Detection ◽  
Model Surface ◽  
Point Cloud Model ◽  
High Level ◽  
Skeleton Point

In order to improve the accuracy of semantic model intrinsic detection, a skeleton-based high-level semantic model intrinsic self-symmetry detection method is proposed. The semantic analysis of the model set is realized by the uniform segmentation of the model within the same style, the component correspondence of the model between different styles, and the shape content clustering. Based on the results of clustering analysis, for a given three-dimensional (3D) point cloud model, according to the curve skeleton, the skeleton point pairs reflecting the symmetry between the model surface points are obtained by the election method, and the symmetry is extended to the model surface vertices according to these skeleton point pairs. With the help of skeleton, the symmetry of the point cloud model is obtained, and then the symmetry region of point cloud model is obtained by the symmetric correspondence matrix and spectrum method, so as to realize the intrinsic symmetry detection of the model. The experimental results show that the proposed method has the advantages of less time, high accuracy, and high reliability.

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