- This paper contains analysis of main modern approaches to dynamic code generation, in particular creation of new classes during program execution. The main attention was paid to universal exploiters of homogeneous classes of objects, which were proposed as a part of such knowledge-representation model as object-oriented dynamic networks, as the tools for creation of new classes of objects in program runtime. As the result, algorithms for implementation of such universal exploiters of classes of objects as union, intersection, difference and symmetric difference were developed. These algorithms can be used in knowledge-based intelligent systems, which are based on object-oriented dynamic networks, and they can be adapted for some object-oriented programming languages with powerful metaprogramming opportunities. INTRODUCTION As the result of intensive development of programming languages and technologies during a few last decades, many new programming techniques, tools, technologies and directions within the area have aroused. One of the important and attractive directions within the modern programming is metaprogramming, the main ideas of which is an ability of programs to analyze, modify and generate codes of other programs, including their own.

Lua is a scripting language developed at the Pontifical Catholic University of Rio de Janeiro (PUC-Rio) that has come to be the leading scripting language for video games worldwide.3,7 It is also used extensively in embedded devices like set-top boxes and TVs and in other applications like Adobe Photoshop Lightroom and Wikipedia.14 Its first version was released in 1993. The current version, Lua 5.3, was released in 2015. Though mainly a procedural language, Lua lends itself to several other paradigms, including object-oriented programming, functional programming, and data-driven programming.5 It also offers good support for data description, in the style of JavaScript and JSON.

The notions of formal contexts and concept lattices, although introduced by Wille only ten years ago, already have proven to be of great utility in various applications such as data analysis and knowledge representation. In this paper we give arguments that Wille's original notion of formal context, although quite appealing in its simplicity, now should be replaced by a more semantic notion. This new notion of formal context entails a modified approach to concept construction. We base our arguments for these new versions of formal context and concept construction upon Wille's philosophical attitude with reference to the intensional aspect of concepts. We give a brief development of the relational theory of formal contexts and concept construction, demonstrating the equivalence of "concept-lattice construction" of Wille with the well-known "completion by cuts" of MacNeille. Generalization and abstraction of these formal contexts offers a powerful approach to knowledge representation.

We contribute the first large-scale dataset of scene sketches, SketchyScene, with the goal of advancing research on sketch understanding at both the object and scene level. The dataset is created through a novel and carefully designed crowdsourcing pipeline, enabling users to efficiently generate large quantities of realistic and diverse scene sketches. SketchyScene contains more than 29,000 scene-level sketches, 7,000+ pairs of scene templates and photos, and 11,000+ object sketches. All objects in the scene sketches have ground-truth semantic and instance masks. The dataset is also highly scalable and extensible, easily allowing augmenting and/or changing scene composition. We demonstrate the potential impact of SketchyScene by training new computational models for semantic segmentation of scene sketches and showing how the new dataset enables several applications including image retrieval, sketch colorization, editing, and captioning, etc. The dataset and code can be found at https://github.com/SketchyScene/SketchyScene.

This paper describes a novel approach to software engineering derived from the "SP Theory of Intelligence" and its realisation in the "SP Computer Model". Despite superficial appearances, it is shown that many of the key ideas in software engineering have counterparts in the structure and workings of the SP system. Potential benefits of this new approach to software engineering include: the automation or semi-automation of software development, with support for programming of the SP system where necessary; allowing programmers to concentrate on 'world-oriented' parallelism, without worries about parallelism to speed up processing; support for the long-term goal of programming the SP system via written or spoken natural language; reducing or eliminating the distinction between 'design' and 'implementation'; reducing or eliminating operations like compiling or interpretation; reducing or eliminating the need for verification of software; reducing the need for validation of software; no formal distinction between program and database; the potential for substantial reductions in the number of types of data file and the number of computer languages; benefits for version control; and reducing technical debt.

We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.

Stefan Jovanovic is a very experienced Android Developer with a demonstrated history of working in the information technology and services industry. Skilled in Android, Solidity, Linux and Object-Oriented Programming (OOP), Stefan Jovanovic led CryptoAngel to be specialized in AI, blockchain, augmented reality, and IoT. JC: CryptoAngel is a very skill-oriented company, in which you have a team of extraordinary creative people dedicated to their mission to enable people to robust their potentials, talents and knowledge using disruptive modern technologies based on AI and Blockchain. Does that mean CryptoAngel offer a very customized service for your customer? Stefan: Crypto Angel offers a customize service for every each costumer.

There has been a recent boon in studies regarding the importance of the theory of affordances [1] in learning and understanding behaviour in human activities. Studies in neuroscience and cognitive science on object affordances indicate that the mirror neurons in human brains congregate visual and motor responses [2], [3], [4]. Mirror neurons in the F5 sector of the macaque ventral pre-motor cortex fire during both observation of interacting with an object and action execution, but do not discharge in response to simply observing an object [5], [6]. Further studies [7] show the functional relationship between paired objects and compared it with the spatial relationship and found that both the position and functional context are important and related to the motion; however, the motor action response was faster and more accurate with the functional context than with the spatial context. Yoon et al. [8] recently studied affordances associated to pairs of objects positioned for action and found an interesting so-called "paired object affordance effect", where the response time by right-handed participants was faster if the two objects were used together, where the active (manipulated) object was to the right of the other. From these studies, it is clear that functional relationships between objects are directly associated with motor actions. This interesting phenomenon can be observed in human daily life: when humans are performing tasks, they not only pay attention to objects and their states but also to object interactions caused by manipulation.

One does not simply walk into TensorFlow. A PhD is a good opportunity for introspection. In fact, it is important to create opportunities for introspection no matter how busy or insignificant the present feels like. We should not regard our past as an immature period, but as an unfolding story. A story of discoveries, mistakes, skills, and projects that are now part of our professional consciousness.

During my years as a Consultant Data Scientist I have received many requests from my clients to provide frequency distribution reports for their specific business data needs. These reports have been very useful for the company management to make proper business decisions quickly. In this paper I would like to show how to design and develop a generic frequency distribution library that will allow you to reduce your development time and provide a good summary table and image report for your clients. One important topic to be covered is this paper is a logic conversion of a top-bottom Python code in a generic reusable super class library for future Object-Oriented Programming (OOP) development applied data analytics and visualization. I'll be using the following three main Python Data Stack libraries: The frequency of a particular data value is the number of times the data value occurs.