OVIDE: A Software Package for Verifying and Validating Petri Nets

This paper presents a symbolic approach for the analysis of bounded Petri nets. The structure and behavior of the Petri net is symbolically modeled by using Boolean functions, thus reducing reasoning about Petri nets to Boolean calculation. The set of reachable markings is calculated by symbolically firing the transitions in the Petri net. Highly concurrent systems suffer from the state explosion problem produced by an exponential increase of the number of reachable states. This state explosion is handled by using Binary Decision Diagrams (BDDs) which are capable of representing large sets of markings with small data structures. Petri nets have the ability to model a large variety of systems and the flexibility to describe causality, concurrency, and conditional relations. The manipulation of vast state spaces generated by Petri nets enables the efficient analysis of a wide range of problems, e.g., deadlock freeness, liveness, and concurrency. A number of examples are presented in order to show how large reachability sets can be generated, represented, and analyzed with moderate BDD sizes. By using this symbolic framework, properties requiring an exhaustive analysis of the reachability graph can be efficiently verified.

Cellular automata and other discrete dynamical systems have long been studied as models of emergent complexity. Recently, neural cellular automata have been proposed as models to investigate the emerge of a more general artificial intelligence, thanks to their propensity to support properties such as self-organization, emergence, and open-endedness. However, understanding emergent complexity in large scale systems is an open challenge. How can the important computations leading to emergent complex structures and behaviors be identified? In this work, we systematically investigate a form of dimensionality reduction for 1-dimensional and 2-dimensional cellular automata based on coarse-graining of macrostates into smaller blocks. We discuss selected examples and provide the entire exploration of coarse graining with different filtering levels in the appendix (available also digitally at this link: https://s4nyam.github.io/eca88/. We argue that being able to capture emergent complexity in AI systems may pave the way to open-ended evolution, a plausible path to reach artificial general intelligence.

Large-scale complex systems (LSS) have traditionally been characterized by large numbers of variables, structure of interconnected subsystems, and other features that complicate the control models such as nonlinearities, time delays, and uncertainties. The decomposition of LSS into smaller, more manageable subsystems allowed for implementing effective decentralization and coordination mechanisms. The last decade revealed new characteristic features of LSS such as the networked structure, enhanced geographical distribution and increased cooperation of subsystems, evolutionary development, and higher risk sensitivity. This chapter aims to present a balanced review of several traditional well-established methods and new approaches together with typical applications. First the hierarchical systems approach is described and the transition from coordinated control to collaborative schemes is highlighted. Three subclasses of methods that are widely utilized in LSS – decentralized control, simulation-based, and artificial-intelligence-based schemes – are then reviewed. Several basic aspects of decision support systems (DSS) that are meant to enable effective cooperation between man and machine and among the humans in charge with LSS management and control are briefly exposed. The chapter concludes by presenting several technology trends in LSS.

Early diagnosis is especially important, because cataracts develop slowly and many patients are not aware of their gradual loss of sight until very late in the disease when vision is irreversibly affected. Therefore, improving methods for cataract screening is an important field of research. This research involves the combination of engineering and medical methods in order to create physical solutions which eliminate suffering caused by cataract-related blindness. These methods must address cost effectiveness and reliability issues [4]. One way to address these issues is to use inexpensive computer technology to design and develop automated cataract classification systems for diagnosis support. Computer-based systems come with their own set of challenges, which result from the immense flexibility of modern processing machines [5]. One of these shortcomings is state space explosion and nondeterminism of large-scale systems. To overcome, or at least manage, these challenges, this type of medical support system must be designed with a formal and model-driven design methodology [6,7].

With the growing demands of mobile IoT applications, unmanned aerial vehicles (UAVs) have attracted increasing attention for their flexibility and mobility, and are being widely employed as aerial edge servers in mobile edge computing (MEC) systems to extend coverage and improve adaptability in dynamic environments. This paper jointly optimizes task offloading, resource allocation, and UAV trajectory planning in a multi-UAV-assisted MEC system, aiming to minimize total energy consumption and delay. To cope with environmental dynamics, reinforcement learning (RL) is employed. Specifically, we propose an Option mechanism based on hierarchical RL, enabling agents to leverage knowledge from peers before decisionmaking, which enhances exploration efficiency and accelerates convergence. Additionally, mean field theory is integrated with neural networks to approximate the collective influence of numerous agents, reducing interaction complexity in large-scale systems. Experimental results verify the effectiveness and scalability of the proposed framework.

Semia: semi-automatic interactive graphic editing tool to annotate ambulatory ECG records.

<div class="section abstract"><div class="htmlview paragraph">The objective of this paper is two-fold. Firstly, provide guidance to best implement end to end traceability from program requirements to physical implementation, and Secondly provide techniques to review and understand large scale complex systems. Even with a Digital Engineering Environment (DEE) being an enabler towards applying Systems Engineering practices to develop large scale complex systems, many organizations are unclear on the methodology for modeling their architectures and enabling stakeholders to easily review, understand and assess those architectures. An architecture can be a conceptual, logical or physical architecture, depending on the system’s lifecycle state. For the context of this paper, the modeling environment is any System’s Modeling Language (SysML) based tool along with modeling tools for electrical, mechanical and software development and product life cycle management tool. The intended audience is any engineering organization defining end-to-end architecture within a DEE, and all stakeholders tasked with reviewing conceptual, logical and physical architecture. The outcome of this paper is to provide engineering organizations with guidance on underlying principles for modeling and understanding or assessing architecture that describe large complex systems.</div></div>

System administrators are end-users too. And as end-users, they develop tools, create web pages, write command-line scripts, use spreadsheets, and repurpose existing tools. In short, they engage in end-user programming activities in support of their systems management work. We examined system administrator practices in software tool development, operations, and maintenance based on ethnographic field studies at service delivery centers and data centers across the United States. Our findings suggest that software practices were mostly informal and collaborative and mixed within formal change processes; tool development and debugging were interleaved with tool use and maintenance as they interacted with live systems; and the complexity of large-scale systems and the risks involved in changing live and critical systems put increased demands on system administrators. We argue that system administrators might benefit from certain software engineering methodologies such as agile software development and software modeling.

DECENTRALIZED CONTROL: AN OVERVIEW

THE ROLE OF INTERACTIVE GRAPHIC TOOLS IN TEACHING CONTROL THEORY

When using interactive graphical tools, users often have to manage a structure, i.e. the arrangement of and relations between the parts or elements of the content. However, the interaction with structures may be complex, and not well integrated with the interaction with the content. Based on contextual inquiries and past work, we have identified a number of concepts and requirements about interaction with structure. We have explored a number of interactive tools and we present one of them in this paper: a new kind of property sheet that relies on the implicit structure of graphics. The interactions with the tool augment the scope of interactions to multiple objects.

The large-scale applicability and use of evolutionary computing for complex real-life systems determined a need to ensure strong analytical and theoretical grounds. The special issue, with respect to these concerns, aims at building a bridge between probability, statistics, set oriented numerics and evolutionary computing. A strong interest for identifying new common and challenging research topics is considered, addressing both theoretical and applied aspects in highly complex systems. Uncertainty, error propagation or poor analysis and design, all may lead to catastrophic failures or losses in, for example, high-sensitivity, large scale complex systems; examples can be found by looking at financial markets, surveillance and defense systems, etc. Therefore, algorithms capable of delivering robust solutions while subject to erroneous or abnormal inputs, resilient to failures or with performance guarantees, are of a foremost importance. To this end, a collection of 13 papers is included in this issue, carefully selected from a total of 80 submissions we received. A series of high impact directions are brought into discussion like the design of efficient algorithms for highly complex systems, new algorithms, e.g. extended compact genetic algorithm, multi-objective artificial physics algorithm or service oriented algorithms, performance measures in dynamic optimization, Bayesian networks learning or highdimensional optimization. Real-world application examples are given towards the end, with a view of mobile large-scale networks and GPGPU-based track-beforedetect systems.

Progressive Modeling: The Process, the Principles, and the Applications

Provision of free, downloadable industry-standard reconfigurable hardware EDA tools is a growing and welcome trend. Low cost, ever-more-complex FPGA-based embedded system development platforms with host interface control are becoming more readily available. This paper describes a series of structured VHDL workshop modules which use the Xilinx ISE Webpack/ModelsimXE electronic design automation (EDA) tools and Digilent Spartan-3 hardware development system. The described course implements an extendable embedded system, which performs a selection of image and audio processing applications, controlled by a host graphical user interface (GUI). A formal structured design, documentation and test methodology is incorporated throughout the course. Enhancements to EDA tool VHDL and diagram creation wizard support are proposed to aid VHDL capture automation and design visualisation. Improved student understanding and achievement level in VHDL programming, digital embedded system design techniques and FPGA implementation has resulted

SeisTool is an interactive graphical tool that allows you to analyse seismograms. It runs under the X window environment. The current version employs an OpenLook graphical user interface. It is designed for convenient display of seismograms and handling of events for routine analysis. It also allows phase picking to be done smoothly. A number of time-series analysis operations have also been implemented. Here, is a brief guide to the operation of SeisTool. The intention of this User`s Guide is not to guide you through SeisTool step by step, (The best way of learning how to use SeisTool is run the program and experiment with it.) but, to present a few basic concepts behind SeisTool to acquaint you with the program. The goal of this prototype is to make analysing seismograms as easy and convenient as browsing a file with a text editor. Think of SeisTool as nothing more than a seismogram data file browser.