Special Session on Industry 4.0

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Stress Propagation in Human-Robot Teams Based on Computational Logic Model

Mission teams are exposed to the emotional toll of life and death decisions. These are small groups of specially trained people supported by intelligent machines for dealing with stressful environments and scenarios. We developed a composite model for stress monitoring in such teams of human and autonomous machines. This modelling aims to identify the conditions that may contribute to mission failure. The proposed model is composed of three parts: 1) a computational logic part that statically describes the stress states of teammates; 2) a decision part that manifests the mission status at any time; 3) a stress propagation part based on standard Susceptible-Infected-Susceptible (SIS) paradigm. In contrast to the approaches such as agent-based, random-walk and game models, the proposed model combines various mechanisms to satisfy the conditions of stress propagation in small groups. Our core approach involves data structures such as decision tables and decision diagrams. These tools are adaptable to human-machine teaming as well.Comment: Submitted to IEEE Aerospace 2023 conferenc

A framework for Model-Driven Engineering of resilient software-controlled systems

AbstractEmergent paradigms of Industry 4.0 and Industrial Internet of Things expect cyber-physical systems to reliably provide services overcoming disruptions in operative conditions and adapting to changes in architectural and functional requirements. In this paper, we describe a hardware/software framework supporting operation and maintenance of software-controlled systems enhancing resilience by promoting a Model-Driven Engineering (MDE) process to automatically derive structural configurations and failure models from reliability artifacts. Specifically, a reflective architecture developed around digital twins enables representation and control of system Configuration Items properly derived from SysML Block Definition Diagrams, providing support for variation. Besides, a plurality of distributed analytic agents for qualitative evaluation over executable failure models empowers the system with runtime self-assessment and dynamic adaptation capabilities. We describe the framework architecture outlining roles and responsibilities in a System of Systems perspective, providing salient design traits about digital twins and data analytic agents for failure propagation modeling and analysis. We discuss a prototype implementation following the MDE approach, highlighting self-recovery and self-adaptation properties on a real cyber-physical system for vehicle access control to Limited Traffic Zones

Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node

Binary Neural Networks (BNNs) have been shown to be robust to random bit-level noise, making aggressive voltage scaling attractive as a power-saving technique for both logic and SRAMs. In this work, we introduce the first fully programmable IoT end-node system-on-chip (SoC) capable of executing software-defined, hardware-accelerated BNNs at ultra-low voltage. Our SoC exploits a hybrid memory scheme where error-vulnerable SRAMs are complemented by reliable standard-cell memories to safely store critical data under aggressive voltage scaling. On a prototype in 22nm FDX technology, we demonstrate that both the logic and SRAM voltage can be dropped to 0.5Vwithout any accuracy penalty on a BNN trained for the CIFAR-10 dataset, improving energy efficiency by 2.2X w.r.t. nominal conditions. Furthermore, we show that the supply voltage can be dropped to 0.42V (50% of nominal) while keeping more than99% of the nominal accuracy (with a bit error rate ~1/1000). In this operating point, our prototype performs 4Gop/s (15.4Inference/s on the CIFAR-10 dataset) by computing up to 13binary ops per pJ, achieving 22.8 Inference/s/mW while keeping within a peak power envelope of 674uW - low enough to enable always-on operation in ultra-low power smart cameras, long-lifetime environmental sensors, and insect-sized pico-drones.Comment: Submitted to ISICAS2020 journal special issu

Resilience: an all-encompassing solution to global problems? A biopolitical analysis of resilience in the policies of EC, FEMA, UNDP, USAID, WB, and WEF

This thesis examines the use of resilience in international policy-making. A concept that originally meant an ability of ecosystems to absorb disturbance has not only been welcomed in many disciplines outside ecology, but lately become popular in the policies of international organisations that claim resilience as a solution to various ‘global problems’ such as climate change, underdevelopment, or economic crises. The study contributes to the ongoing critical discussion on the governance effects of resilience. Here, the Foucauldian theory of biopolitics and the concept of governmentality are useful. Resilience now addresses human systems and communities with concepts from natural sciences, thus making it a biopolitical phenomenon. Specifically, the thesis asks how mainstreaming resilience affects the pursuit of agendas in six organisations: European Commission, Federal Emergency Management Agency, United Nations Development Programme, United States Agency for International Development, World Bank, and World Economic Forum. Using Foucauldian discourse analysis, the study is thematically divided into adaptive, entrepreneurial and governing aspects of resilience. Each part explicates how truth, power and subjectivity are constructed in the discourse. The analysis shows that contrary to the policy claims, resilience does not function as a solution but is constitutive of the problems it attempts to solve. The current policy discourse confirms pre-existing practices and power relations, and further problematizes issues on the agendas. The thesis confirms that the policies are trapped in a neoliberal biopolitics that has problematic implications for human subjectivity and political agency. It further concludes that if resilience is to have any practical relevance and positive effects, the policy discourse has to be changed, for which current critical accounts do not offer a plausible direction. Therefore, a distinction between resilience as a policy tool and social resilience is needed, whereby the use of resilience as a policy solution is reduced to disaster risk reduction and similar technical functions, and social resilience is recognised as a communal capacity that cannot be subject to policy regulation

A Rely-Guarantee Specification of Mixed-Criticality Scheduling

The application considered is mixed-criticality scheduling. The core formal approaches used are Rely-Guarantee conditions and the Timeband framework; these are applied to give a layered description of job scheduling which includes resilience to jobs overrunning their expected execution time. A novel formal modelling idea is proposed to handle the relationship between actual time and its approximation in hardware clocks.Comment: This paper will appear in a Festschrift - on publication we will insert a pointer to the boo