Tag Archives: Team Research Transfer

Fastlane

Nowadays Web applications, including 3D virtual worlds, real-time simulations, and virtual reality applications demand a high amount of processing power. JavaScript, yet, is inherently single threaded and computationally intensive tasks need to avoid taking exclusive control over this thread for a prolonged time to not stall the entire Web page.

The JavaScript APIs that enable hardware-supported parallelism, such as SIMD.js and Web Worker, however, are by design low-level APIs and subject to hardware specific limitation, unfamiliar programming idioms, and performance portability issues if they are not available on every platform.

Fastlane solves these problems by combining the results of two successful projects, Xflow and shade.js, to provide a compiler-driven adaptive data-flow-programming framework for parallel data processing on the web.

It utilizes data-flow programming, a proven and well-know programming idiom for data processing, to define a series of data-transformations, each written in a valid subset of JavaScript. The provided data-flow graph is then analyzed and compiled into optimized JavaScript or GLSL shader code, considering all APIs available on the current system. This alleviates the necessity for the developer to define multiple versions of the same computation for different combination of available APIs and platform features.

CIMPLEX

Results

In the EU project CIMPLEX the research department of Agents and Simulated Reality (ASR) created solutions to visualize large amounts of diverse data. This allows for the analysis in the field of epidemic spreading of diseases.
The data of the disease spreading comes from two sources: Either recorded data like travel information or simulated data.
This data can be visualized using different visualization techniques e.g. network graphs to allow for easy detection of relationships and disease spreading over time. All views of the data can be synchronized.
Moreover users can work with the data in a collaborative manner using different devices like classical desktop web applications or more modern devices like tablets or VR and AR headsets.
Through data flow programming and novel technologies a data-parallel as well as thread-parallel processing is made possible. Hardware computing and rendering increases performance.

Motivation

Epidemics are an international problem, more likely than ever before. Their course is complex and difficult to foresee. The past has shown how important a quick reaction for the effectiveness of countermeasures is.
One example from the project is the spreading of influenza. This disease usually occurs in winter time. The dry air and the use of air conditioning fostering its growth. International travel than increases it’s spread across boundaries.
It is essential to gain a comprehensive and up-to-date picture of the crisis situation from the outset, to analyze the situation and to communicate the necessary measures quickly and purposefully.

Goals

One aim of the project was to improve the communication with the affected persons while delivering an effective and purposeful disaster management at the same time.
New communication technologies, which are based on social networks and smartphones, can help to deliver and link the necessary information.
The tools and computer models that were developed were intended to inform decision-makers and citizens in real-time and support them in combating disease spread.
The technologies employed were threefold. First large scale, realistic, data-driven models to predict the disease spread. Second participatory data collection to gain information about disease outbreaks early on and last advanced methods for Big Data analysis and visualization.
In the European research project CIMPLEX (Bringing of Citizen, model and Data together in Participatory, Interactive Social Exploratories) such a new system was developed.
CIMPLEX exploratory concept
CIMPLEX exploratory concept

Challenges

CIMPLEX combined information from a variety of sources such as social networks, cell phone positions, the social and economic environment, and the experiences and opinions of eyewitnesses.
New models for explanation, visualization and interaction with data and models both on individual and on collective level were to be developed. Theoretical, methodological and technological advances were aimed at in order to better foresee, explain and handle disease spreading.
All this were to be molded into a broadly usable ICT platform.

Solution

Objectives

The proposed solution should be usable by a wide range of users. From policy makers trying to curb disease spread, researchers developing news models for disease spread prediction up to citizens.
The visualization should be able to yield different views on the underlying data and models and allowing for a collaborative analysis.
The visualization must be able to handle vast amounts of geo-referenced, data time-dependent data.
Moreover a custom deployment for domain specific use cases must be possible in order to maintain the flexibility of the system

Requirements

To maximize the range of possible users the web was used as a target platform. It is widely spread, most users are familiar to it, it is available on many devices and supports classical 3D visualization as well as 3D and VR.
It also supports a wider range of user interactions. Ranging from usual keyboard and mouse interaction it also supports touch devices and more elaborate VR controllers.
Since the planned system would build on a plethora of possible data sources a service oriented architecture (SOA) was proposed. Thus easing reuse of components and independent development of services.

Architecture

The architecture of the final system consisted of three layers.
The first layer was data acquisition. It delivered data from social sensing components as well as participatory data collection.
The second layer was models and simulation. It used epidemic simulation web services as well as integrated computational models.
The third layer was the exploratory layer. This layer allows for the visual exploration of the combined data sources from the first two layers.
CIMPLEX three layer architecture
CIMPLEX three layer architecture

Technologies

Visualization Framework

The Visualization Framework was developed in a cooperation between the University of Stuttgart and the DFKI. The highly customizable web-based open-source framework allows to connect to different web services, and to analyze the data with a large variety of interactive visualizations that are connected via brushing and linking. The web-based applications created with the framework run on multiple devices such as smartphones, tablets, desktop computers and large display walls.

The Visualization Framework runs on a multitude of devices e.g. display walls

An interactive live demo can be found here: https://github.com/cimplex-project/visualization-framework

Globe Library

DFKI developed a WebGL-based standalone library to visualize the different simulation data on a 3D globe in the browser. The library contains a lightweight interface to create a3D globe that supports 2D and 3D projections as well as custom tilesets with dynamic zoom levels with the ability to add, remove, and change transitions and basin values efficiently and in real-time at run time. The library makes use of available platform specific input methods e.g. touches on mobile, where the user is able to use pinch and drag gestures to interact with the globe. It utilizes Fastlane, a JavaScript library for data-flow base parallel data processing for the web, which is being developed from DFKI in collaboration with an industry partner in related project. The library offers an abstraction over low-level APIs that expose hardware parallelism (SIMD.js, GLSL), thus making enabling its use even by non-experts. It is used, in particular, to implement the real-time processing of huge datasets for visualization with WebGL.

A high performance JavaScript library for visualizing data on an interactive globe

A interactive live demo can be found here: https://cimplex-project.github.io/cimplex-globe.

Decoder Library

DFKI in collaboration with ISI developed a user-friendly JavaScript open source library for fast data exchange. The library utilizes parallel data processing (web workers) and offers an asynchronous interface. It is able to create, remove and decode simulation data using the GleamViz web service.

The GLEAMViz-decoder library acts as a middleware between simulation services and the Visualization Framework.

The GLEAMViz web service hosts simulation data in various formats. All of them contain compressed binary data for fast data exchange and that needs to be decoded on the client in order to be processed and visualized. By using parallel data processing the decoding time can be greatly reduced. Thus,the library offers decoders for GLEAMViz from ISI and agent based model datasets from ISI and FBK, including movement data from the DFKI.

Configurator

We also have developed a configurator application that tremendously reduces the time to configure and deploy a custom version of the visualization framework including all data and simulation services. Based on a web page URL, a user is now able to select, which views, data and simulation services he wants to include in his custom deployment. The server backend then creates a unique executable file depending on the selections, and offers it for downloading. The downloaded executable, based on node.js and Docker, then fully automatically installs all dependencies, including data and simulation services, locally on the client machine. By using Docker, the installation is isolated and does not affect or modify the client host system. The configurator creates executables for Windows, Mac OSX and Linux.

The three different steps of the Web-based CIMPLEX Configurator. The user is able to select which views, data and simulation services he wants to deploy locally.

Supplemental

  • Title: Bringing of Citizen, model and Data together in Participatory, Interactive Social Exploratories
  • Run Time: 01.01.2015 – 31.12.2017
  • Funding:
    • FET Proactive Global Systems Science (GSS)
    • Grant agreement no: 641191
  • Partners
    • Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Germany
    • Eidgenössische Technische Hochschule Zürich, Switzerland
    • Universität Stuttgart, Germany
    • University College London, United Kingdom
    • Közép-európai Egyetem (Central European University), Hungary
    • Fondazione Istituto per l’Interscambio Scientifico, Italy
    • Consiglio Nazionale delle Ricerche, Italy
    • Fondazione Bruno Kessler, Italy
  • Media
    • YouTube-Channel
      • https://www.youtube.com/channel/UC0pWNHUogTZRHuAspF-5WKw
    • Video Globe
      • https://www.youtube.com/channel/UC0pWNHUogTZRHuAspF-5WKw

– Hybr-iT

Hybr-iT

Hybrid and intelligent human-robot collaboration – hybrid teams in versatile, cyber-physical production environments

The aim of the Hybr-iT joint research project funded by the Federal Ministry of Education and Research (BMBF) is to build and test hybrid teams of humans and robots working together with software-based assistance systems in intelligent environments in industrial manufacturing. Based on a holistic approach to the various disciplines of human-robot collaboration, intelligent planning and simulation environments, assistance systems and knowledge-based robotics, workers in the production process are supported by robots in such a way that this intensive human-robot cooperation is convenient, safe and efficient.


Hybr-iT researches and evaluates the components required for planning and optimizing hybrid teams in an industrial context – in terms of their integration in existing IT and production systems and as necessary for their control in a production operation. From an IT perspective, this will involve heavily distributed systems with very heterogeneous subsystems (such as plant and robot controls, safety, logistic, database, assistance, tracking, simulation, and visualization systems), which are implemented together in a comprehensive resource oriented architecture (ROA). ASR contributes to the ROA and develops the simulation environment for hybrid human-robot teams, using AJAN and Motion Synthesis.

The Hybr-iT project is funded by the Federal Ministry of Education and Research.

Ansprechpartner: Ingo Zinnikus

AJAN

AJAN (Access Java Agent Nucleus) is a modular agent web service which integrates different AI technologies in an intuitive way for creating autonomous systems. The main goal of the development is to address a heterogeneous community with an easy to use, flexible and powerful AI tool for different domains, such like 3D simulations, programmable web or home automation. AJAN is in use for different virtual reality applications, such as pedestrian or shop floor simulations (in the context of Industrie 4.0) in which multiple autonomous 3D entities has to be controlled.
Continue reading AJAN

HYSOCIATEA

Hybrid Social Teams for Long-Term Collaboration in Cyber-Physical Environments

HySociaTea is short for Hybrid SociaTeams for Long-Term Collaboration in Cyber-Physical Systems. In this project, funded by the German Ministry for Education and Research, the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum für Künstliche Intelligenz – DFKI) investigates how teams of humans, robots and software agents (i.e. virtual characters and softbots) can work collaboratively in a production scenario.

Many large-scale real-world problems, such as effective disaster response, the careful dismantling of contaminated structures or the efficient manufacturing and construction of complex artifacts, require the coordination of such teams of humans, robots, and software agents to accomplish the collection of challenging tasks. It is well known that more can be achieved through teamwork in a shorter timeframe and at a higher quality than by individual performance. Our vision of effective hybrid social teams of humans, robots and software agents working together seamlessly in dynamic cyber-physical environments can only be achieved by combining the research excellence and experience of eight DFKI research departments located at the three main sites (Bremen, Kaiserslautern, Saarbrücken) in a joint effort. HySociaTea is thus a collaborative grand challenge project, which combines the diverse DFKI competencies in a wide spectrum of subfields of Artificial Intelligence to address a fundamental research goal in the area of intelligent interacting agents.

The envisioned characteristics of hybrid social teams of physical and virtual agents imply the following fundamental research challenges:

  1. Distributed problem analysis and task allocation based on skills, knowledge and experience
  2. Sharing goals, plans as well as intentions and coordinating plan execution
  3. Understanding all physical and communicative interactions of all team members
  4. Developing social group behavior and emotional coherence
  5. Building mutual trust and demonstrating accountability for the assigned subtasks
  6. Compensating weaknesses of individual team members by empathetic help

Project Homepage: http://hysociatea.dfki.de/

Contact: Ingo Zinnikus


Deutsche Version

Das vom Bundesministerium für Bildung und Forschung (BMBF) geförderte Projekt HySociaTea (Hybrid Social Teams for Long-Term Collaboration in Cyber-Physical Environments) realisiert und untersucht die Zusammenarbeit von technologisch unterstützten Menschen mit autonomen Robotern, virtuellen Avataren und Softbots, die zusammen in einem Team gemeinsame Aufgaben lösen sollen.

Im Zusammenhang mit dem Zukunftsprojekt Industrie 4.0 ermöglichen diese hybriden Teams z.B. eine flexible Produktion, in der auch auf ungeplante Ereignisse durch eigenständige Reorganisation des Teams reagiert werden kann. Neben der Erforschung der reinen technischen Grundlagen, liegt der Schwerpunkt auch auf der Entwicklung von robotischen Teamkompetenzen, sowie auf intelligentem Multi-Agenten-Verhalten, welche auch wichtige Aspekte in rein menschlichen Teams sind. Technische Systeme sollen hier vor allem als Assistenzsystem für den Menschen in der Produktion eingesetzt werden – die Roboter müssen also als Partner im Gesamtprozess wahrgenommen werden.

Der in HySociaTea entwickelte und untersuchte hybride Teamaufbau kann auf lange Sicht in unterschiedlichen realen Herausforderungen eingesetzt werden, z.B. bei modularen Produktionsanlagen in der Fabrik der Zukunft, als Rettungsteam bei Katastrophenszenarien, oder bei der notwendigen Arbeitsteilung zwischen Menschen und Maschinen beim sicheren Rückbau von Atomkraftwerken.

Zur Realisierung des Projekts bündeln verschiedene Fachbereiche aus allen DFKI-Standorten (Bremen, Kaiserslautern, Saarbrücken) ihre Kompetenzen:

  • RIC (Robotics Innovations Center, DFKI Bremen): autonome und kooperative Robotersysteme, mobile Manipulation
  • CPS (Cyber-Physical Systems, DFKI Bremen): sichere Mensch-Roboter Interaktion
  • EI (Embedded Intelligence, DFKI Kaiserslautern): technische Einbindung des Menschen, tragbare Sensorik
  • AV (Augmented Vision, DFKI Kaiserslautern): Perzeptionsmodule mittels Bildverarbeitung und Sensorfusion
  • KM (Knowledge Management, DFKI Kaiserslautern): blickgesteuerte Aufmerksamkeitserkennung, Realzeit-Objekterkennung
    IUI (Intelligent User Interfaces, DFKI Saarbrücken): emo-soziale virtuelle Charaktere, multimodale Dialogplattform
  • LT (Language Technology Lab, DFKI Saarbrücken): autonome Teamreorganisation, Sprachinteraktion
  • ASR (Agents and Simulated Reality, DFKI Saarbrücken): Kommunikations-Middleware, Dual Reality

HySociaTea wird gefördert durch das Bundesministerium für Bildung und Forschung (BMBF) unter Förderkennzeichen 01IW14001.

Project Homepage: http://hysociatea.dfki.de/

Ansprechpartner: Ingo Zinnikus