Event-Cloud Platform to Support Decision- Making in Emergency Management

The challenge of this paper is to underline the capability of an Event-Cloud Platform to support efficiently an emergency situation. We chose to focus on a nuclear crisis use case. The proposed approach consists in modeling the business processes of crisis response on the one hand, and in supporting the orchestration and execution of these processes by using an Event-Cloud Platform on the other hand. This paper shows how the use of Event-Cloud techniques can support crisis management stakeholders by automatizing non-value added tasks and by directing decision- makers on what really requires their capabilities of choice. If Event-Cloud technology is a very interesting and topical subject, very few research works have considered this to improve emergency management. This paper tries to fill this gap by considering and applying these technologies on a nuclear crisis use-case

CHORUS Deliverable 4.5: Report of the 3rd CHORUS Conference

The third and last CHORUS conference on Multimedia Search Engines took place from the 26th to the 27th of May 2009 in Brussels, Belgium. About 100 participants from 15 European countries, the US, Japan and Australia learned about the latest developments in the domain. An exhibition of 13 stands presented 16 research projects currently ongoing around the world

Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models

Natural-language dialog is key for intuitive human-robot interaction. It can be used not only to express humans' intents, but also to communicate instructions for improvement if a robot does not understand a command correctly. Of great importance is to endow robots with the ability to learn from such interaction experience in an incremental way to allow them to improve their behaviors or avoid mistakes in the future. In this paper, we propose a system to achieve incremental learning of complex behavior from natural interaction, and demonstrate its implementation on a humanoid robot. Building on recent advances, we present a system that deploys Large Language Models (LLMs) for high-level orchestration of the robot's behavior, based on the idea of enabling the LLM to generate Python statements in an interactive console to invoke both robot perception and action. The interaction loop is closed by feeding back human instructions, environment observations, and execution results to the LLM, thus informing the generation of the next statement. Specifically, we introduce incremental prompt learning, which enables the system to interactively learn from its mistakes. For that purpose, the LLM can call another LLM responsible for code-level improvements of the current interaction based on human feedback. The improved interaction is then saved in the robot's memory, and thus retrieved on similar requests. We integrate the system in the robot cognitive architecture of the humanoid robot ARMAR-6 and evaluate our methods both quantitatively (in simulation) and qualitatively (in simulation and real-world) by demonstrating generalized incrementally-learned knowledge.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Submitted to the 2023 IEEE/RAS International Conference on Humanoid Robots (Humanoids). Supplementary video available at https://youtu.be/y5O2mRGtsL

A Domain Specific Language for Digital Forensics and Incident Response Analysis

One of the longstanding conceptual problems in digital forensics is the dichotomy between the need for verifiable and reproducible forensic investigations, and the lack of practical mechanisms to accomplish them. With nearly four decades of professional digital forensic practice, investigator notes are still the primary source of reproducibility information, and much of it is tied to the functions of specific, often proprietary, tools. The lack of a formal means of specification for digital forensic operations results in three major problems. Specifically, there is a critical lack of: a) standardized and automated means to scientifically verify accuracy of digital forensic tools; b) methods to reliably reproduce forensic computations (their results); and c) framework for inter-operability among forensic tools. Additionally, there is no standardized means for communicating software requirements between users, researchers and developers, resulting in a mismatch in expectations. Combined with the exponential growth in data volume and complexity of applications and systems to be investigated, all of these concerns result in major case backlogs and inherently reduce the reliability of the digital forensic analyses. This work proposes a new approach to the specification of forensic computations, such that the above concerns can be addressed on a scientific basis with a new domain specific language (DSL) called nugget. DSLs are specialized languages that aim to address the concerns of particular domains by providing practical abstractions. Successful DSLs, such as SQL, can transform an application domain by providing a standardized way for users to communicate what they need without specifying how the computation should be performed. This is the first effort to build a DSL for (digital) forensic computations with the following research goals: 1) provide an intuitive formal specification language that covers core types of forensic computations and common data types; 2) provide a mechanism to extend the language that can incorporate arbitrary computations; 3) provide a prototype execution environment that allows the fully automatic execution of the computation; 4) provide a complete, formal, and auditable log of computations that can be used to reproduce an investigation; 5) demonstrate cloud-ready processing that can match the growth in data volumes and complexity

Advancing Smart Manufacturing in Europe: Experiences from Two Decades of Research and Innovation Projects

In the past two decades, a large amount of attention has been devoted to the introduction of smart manufacturing concepts and technologies into industrial practice. In Europe, these efforts have been supported by European research and innovation programs, bringing together research and application parties. In this paper, we provide an overview of a series of four content-wise connected projects on the European scale that are aimed at advancing smart manufacturing, with a focus on connecting processes on smart factory shop floors to manufacturing equipment on the one hand and enterprise-level business processes on the other hand. These projects cover several tens of application cases across Europe. We present our experiences in the form of a single, informal longitudinal case study, highlighting both the major advances and the current limitations of developments. To organize these experiences, we place them in the context of the well-known RAMI4.0 reference framework for Industry 4.0 (covering the ISA-95 standard). Then, we analyze the experiences, both the positive ones and those including problems, and draw our learnings from these. In doing so, we do not present novel technological developments in this paper—these are presented in the papers we refer to—but concentrate on the main issues we have observed to guide future developments in research efforts and industrial innovation in the smart industry domain

Affective processes as network hubs

The practical problems of designing and coding a web-based flight simulator for teachers has led to a ‘three-tier plus environment’ model (COVE model) for a software agent’s cognition (C), psychologicsal (O), physical (V) processes and responses to tasks and interpersonal relationships within a learning environment (E). The purpose of this article is to introduce how some of the COVE model layers represent preconscious processing hubs in an AI human-agent’s representation of learning in a serious game, and how an application of the Five Factor Model of psychology in the O layer determines the scope of dimensions for a practical computational model of affective processes. The article illustrates the model with the classroom-learning context of the simSchool application (www.simschool.org); presents details of the COVE model of an agent’s reactions to academic tasks; discusses the theoretical foundations; and outlines the research-based real world impacts from external validation studies as well as new testable hypotheses of simSchool

Subsidiary roles and dual knowledge flows between MNE subsidiaries and headquarters : The moderating effects of organizational governance types

In this paper, we investigate how various types of subsidiary roles affect dual knowledge flows between a focal subsidiary and the multinational enterprise’s headquarters, a thus far overlooked topic in the knowledge management literature. We propose that subsidiaries with a world mandate have a stronger positive impact on dual knowledge flows than subsidiaries with a specialized contributor role. In contrast, we argue that subsidiaries with a local implementer role have a negative impact on dual knowledge flows. Further, we investigate the moderating effect of two different organizational governance types, namely, belonging to a South Korean business group (i.e., Chaebol), and being a small and medium-sized enterprise. Overall, our results from a sample of 1213 foreign manufacturing subsidiaries from 191 South Korean MNEs provide empirical evidence that validates our hypotheses.Peer reviewe

Developments of 5G Technology

This technology is the future of current LTE technology which would be a boost to the future of wireless and computer networks, as the speeds would be way higher than the current LTE networks, which will push the technology to a new level. This technology will make the radio channels to support data access speeds up to 10 Gb/s which will turn the bandwidth radio channels as WiFi. Comparing it with other LTE technology\u27s it has high speed and capacity, support interactive multimedia, voice, internet and its data rate is 1 Gbps which makes it faster than other LTE’s . This is much more effective than other technology’s due to its advanced billing interfaces. This paper provides detail explanation of 5G technology, its architecture, challenges, advantages and disadvantages, issues and ends with future of 5G technology

BPMN4sML: A BPMN Extension for Serverless Machine Learning. Technology Independent and Interoperable Modeling of Machine Learning Workflows and their Serverless Deployment Orchestration

Machine learning (ML) continues to permeate all layers of academia, industry and society. Despite its successes, mental frameworks to capture and represent machine learning workflows in a consistent and coherent manner are lacking. For instance, the de facto process modeling standard, Business Process Model and Notation (BPMN), managed by the Object Management Group, is widely accepted and applied. However, it is short of specific support to represent machine learning workflows. Further, the number of heterogeneous tools for deployment of machine learning solutions can easily overwhelm practitioners. Research is needed to align the process from modeling to deploying ML workflows. We analyze requirements for standard based conceptual modeling for machine learning workflows and their serverless deployment. Confronting the shortcomings with respect to consistent and coherent modeling of ML workflows in a technology independent and interoperable manner, we extend BPMN's Meta-Object Facility (MOF) metamodel and the corresponding notation and introduce BPMN4sML (BPMN for serverless machine learning). Our extension BPMN4sML follows the same outline referenced by the Object Management Group (OMG) for BPMN. We further address the heterogeneity in deployment by proposing a conceptual mapping to convert BPMN4sML models to corresponding deployment models using TOSCA. BPMN4sML allows technology-independent and interoperable modeling of machine learning workflows of various granularity and complexity across the entire machine learning lifecycle. It aids in arriving at a shared and standardized language to communicate ML solutions. Moreover, it takes the first steps toward enabling conversion of ML workflow model diagrams to corresponding deployment models for serverless deployment via TOSCA.Comment: 105 pages 3 tables 33 figure

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio