The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene representation and translates sentences into executable, symbolic programs. To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to. Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences. We use curriculum learning to guide the searching over the large compositional space of images and language. Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences. Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains. It also empowers applications including visual question answering and bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu

Role-Modeling in Round-Trip Engineering for Megamodels

Software is becoming more and more part of our daily life and makes it easier, e.g., in the areas of communication and infrastructure. Model-driven software development forms the basis for the development of software through the use and combination of different models, which serve as central artifacts in the software development process. In this respect, model-driven software development comprises the process from requirement analysis through design to software implementation. This set of models with their relationships to each other forms a so-called megamodel. Due to the overlapping of the models, inconsistencies occur between the models, which must be removed. Therefore, round-trip engineering is a mechanism for synchronizing models and is the foundation for ensuring consistency between models. Most of the current approaches in this area, however, work with outdated batch-oriented transformation mechanisms, which no longer meet the requirements of more complex, long-living, and ever-changing software. In addition, the creation of megamodels is time-consuming and complex, and they represent unmanageable constructs for a single user. The aim of this thesis is to create a megamodel by means of easy-to-learn mechanisms and to achieve its consistency by removing redundancy on the one hand and by incrementally managing consistency relationships on the other hand. In addition, views must be created on the parts of the megamodel to extract them across internal model boundaries. To achieve these goals, the role concept of Kühn in 2014 is used in the context of model-driven software development, which was developed in the Research Training Group 'Role-based Software Infrastructures for continuous-context-sensitive Systems.' A contribution of this work is a role-based single underlying model approach, which enables the generation of views on heterogeneous models. Besides, an approach for the synchronization of different models has been developed, which enables the role-based single underlying model approach to be extended by new models. The combination of these two approaches creates a runtime-adaptive megamodel approach that can be used in model-driven software development. The resulting approaches will be evaluated based on an example from the literature, which covers all areas of the work. In addition, the model synchronization approach will be evaluated in connection with the Transformation Tool Contest Case from 2019

Configurable nD-visualization for complex Building Information Models

With the ongoing development of building information modelling (BIM) towards a comprehensive coverage of all construction project information in a semantically explicit way, visual representations became decoupled from the building information models. While traditional construction drawings implicitly contained the visual representation besides the information, nowadays they are generated on the fly, hard-coded in software applications dedicated to other tasks such as analysis, simulation, structural design or communication. Due to the abstract nature of information models and the increasing amount of digital information captured during construction projects, visual representations are essential for humans in order to access the information, to understand it, and to engage with it. At the same time digital media open up the new field of interactive visualizations. The full potential of BIM can only be unlocked with customized task-specific visualizations, with engineers and architects actively involved in the design and development process of these visualizations. The visualizations must be reusable and reliably reproducible during communication processes. Further, to support creative problem solving, it must be possible to modify and refine them. This thesis aims at reconnecting building information models and their visual representations: on a theoretic level, on the level of methods and in terms of tool support. First, the research seeks to improve the knowledge about visualization generation in conjunction with current BIM developments such as the multimodel. The approach is based on the reference model of the visualization pipeline and addresses structural as well as quantitative aspects of the visualization generation. Second, based on the theoretic foundation, a method is derived to construct visual representations from given visualization specifications. To this end, the idea of a domain-specific language (DSL) is employed. Finally, a software prototype proofs the concept. Using the visualization framework, visual representations can be generated from a specific building information model and a specific visualization description.Mit der fortschreitenden Entwicklung des Building Information Modelling (BIM) hin zu einer umfassenden Erfassung aller Bauprojektinformationen in einer semantisch expliziten Weise werden Visualisierungen von den Gebäudeinformationen entkoppelt. Während traditionelle Architektur- und Bauzeichnungen die visuellen Reprä̈sentationen implizit als Träger der Informationen enthalten, werden sie heute on-the-fly generiert. Die Details ihrer Generierung sind festgeschrieben in Softwareanwendungen, welche eigentlich für andere Aufgaben wie Analyse, Simulation, Entwurf oder Kommunikation ausgelegt sind. Angesichts der abstrakten Natur von Informationsmodellen und der steigenden Menge digitaler Informationen, die im Verlauf von Bauprojekten erfasst werden, sind visuelle Repräsentationen essentiell, um sich die Information erschließen, sie verstehen, durchdringen und mit ihnen arbeiten zu können. Gleichzeitig entwickelt sich durch die digitalen Medien eine neues Feld der interaktiven Visualisierungen. Das volle Potential von BIM kann nur mit angepassten aufgabenspezifischen Visualisierungen erschlossen werden, bei denen Ingenieur*innen und Architekt*innen aktiv in den Entwurf und die Entwicklung dieser Visualisierungen einbezogen werden. Die Visualisierungen müssen wiederverwendbar sein und in Kommunikationsprozessen zuverlässig reproduziert werden können. Außerdem muss es möglich sein, Visualisierungen zu modifizieren und neu zu definieren, um das kreative Problemlösen zu unterstützen. Die vorliegende Arbeit zielt darauf ab, Gebäudemodelle und ihre visuellen Repräsentationen wieder zu verbinden: auf der theoretischen Ebene, auf der Ebene der Methoden und hinsichtlich der unterstützenden Werkzeuge. Auf der theoretischen Ebene trägt die Arbeit zunächst dazu bei, das Wissen um die Erstellung von Visualisierungen im Kontext von Bauprojekten zu erweitern. Der verfolgte Ansatz basiert auf dem Referenzmodell der Visualisierungspipeline und geht dabei sowohl auf strukturelle als auch auf quantitative Aspekte des Visualisierungsprozesses ein. Zweitens wird eine Methode entwickelt, die visuelle Repräsentationen auf Basis gegebener Visualisierungsspezifikationen generieren kann. Schließlich belegt ein Softwareprototyp die Realisierbarkeit des Konzepts. Mit dem entwickelten Framework können visuelle Repräsentationen aus jeweils einem spezifischen Gebäudemodell und einer spezifischen Visualisierungsbeschreibung generiert werden

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Using domain specific language and sequence to sequence models as a hybrid framework for a natural language interface to a database solution

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe aim of this project is to provide a new approach to solving the problem of converting natural language into a language capable of querying a database or data repository. This problem has been around for a while, in the 1970's the US Navy developed a solution called LADDER and since then there have been an array of solutions, approaches and tweaks that have kept the research community busy. The introduction of electronic assistants into the smart phone in 2010 has given new impetus to this problem. With the increasingly pervasive nature of data and its ever expanding use to answer questions within business science, medicine extracting data is becoming more important. The idea behind this project is to make data more democratised by allowing access to it without the need for specialist languages. The performance and reliability of converting natural language into structured query language can be problematic in handling nuances that are prevalent in natural language. Relational databases are not designed to understand language nuance. This project introduces the following components as part of a holistic approach to improving the conversion of a natural language statement into a language capable of querying a data repository. ● The idea proposed in this project combines the use of sequence to sequence models in conjunction with the natural language part of speech technologies and domain specific languages to convert natural language queries into SQL. The approach being proposed by this chapter is to use natural language processing to perform an initial shallow pass of the incoming query and then use Google's Tensor Flow to refine the query with the use of a sequence to sequence model. ● This thesis is also proposing to use a Domain Specific Language (DSL) as part of the conversion process. The use of the DSL has the potential to allow the natural language query to be translated into more than just an SQL statement, but any query language such as NoSQL or XQuery