Intelligent Robotic Process Automation: Generating Executable RPA Scripts from Unsegmented UI Logs

Robotic Process Automation (RPA) is an automation technology that sits between the fields of Business Process Management (BPM) and Artificial Intelligence (AI) that creates software (SW) robots to partially or fully automate rule-based and repetitive tasks (or simply routines) performed by human users in their applications’ user interfaces (UIs). RPA tools are able to capture in dedicated UI logs the execution of many routines of interest. A UI log consists of user actions that are mixed in some order that reflects the particular order of their execution by the user, thus potentially belonging to different routines. When considering state-of-the-art RPA technology in the BPM domain, it becomes apparent that the current generation of RPA tools is driven by predefined rules and manual configurations made by expert users rather than intelligent techniques. In this paper, we discuss our research targeted at injecting intelligence into RPA practices. Specifically, we present an approach to: (i) automatically understand which user actions contribute to which routines inside a UI log (this issue is known as segmentation) and (ii) automatically generate executable RPA scripts directly from the UI logs that record the user interactions with the SW applications involved in a routine execution, thus skipping completely the (manual) modeling activity of the flowchart diagrams

Simulations of Galactic Cosmic Rays Impacts on the Herschel/PACS Photoconductor Arrays with Geant4 Code

We present results of simulations performed with the Geant4 software code of the effects of Galactic Cosmic Ray impacts on the photoconductor arrays of the PACS instrument. This instrument is part of the ESA-Herschel payload, which will be launched in late 2007 and will operate at the Lagrangian L2 point of the Sun-Earth system. Both the Satellite plus the cryostat (the shield) and the detector act as source of secondary events, affecting the detector performance. Secondary event rates originated within the detector and from the shield are of comparable intensity. The impacts deposit energy on each photoconductor pixel but do not affect the behaviour of nearby pixels. These latter are hit with a probability always lower than 7%. The energy deposited produces a spike which can be hundreds times larger than the noise. We then compare our simulations with proton irradiation tests carried out for one of the detector modules and follow the detector behaviour under 'real' conditions.Comment: paper submitted to Experimental Astronomy in March 200

Renewable sources urban cells microgrid. A case study

Nowadays, microgrid technologies play a relevant role in the research field as well as in the commercial market. The opportunity to provide electricity in wide areas without using centralized electrical infrastructure networks is a reliable key for achieving the European Union sustainability goals. In this regard, the proposed research aims at describing an electric microgrid configuration powered by a photovoltaic system, supplying three school buildings located in the center of Italy. Additionally, the resilience theme is deeply investigated, analyzing the use of an emergency generator system (EGS) in case of electric grid blackouts. MATLAB/Simulink was chosen to simulate the users’ energy demand as well as to calculate the microgrid performance. Results show that almost the total consumption of the microgrid is covered by the photovoltaic system, and the use of an EGS allows energy resilience and moderate economic savings for the communit

The application of Geant4 simulation code for brachytherapy treatment

Brachytherapy is a radiotherapeutic modality that makes use of radionuclides to deliver a high radiation dose to a well-defined volume while sparing surrounding healthy structures. At the National Institute for Cancer Research of Genova a High Dose Rate remote afterloading system provides Ir(192) endocavitary brachytherapy treatments. We studied the possibility to use the Geant4 Monte Carlo simulation toolkit in brachytherapy for calculation of complex physical parameters, not directly available by experiment al measurements, used in treatment planning dose deposition models

Improved Fast Neutron Spectroscopy via Detector Segmentation

Organic scintillators are widely used for fast neutron detection and spectroscopy. Several effects complicate the interpretation of results from detectors based upon these materials. First, fast neutrons will often leave a detector before depositing all of their energy within it. Second, fast neutrons will typically scatter several times within a detector, and there is a non-proportional relationship between the energy of, and the scintillation light produced by, each individual scatter; therefore, there is not a deterministic relationship between the scintillation light observed and the neutron energy deposited. Here we demonstrate a hardware technique for reducing both of these effects. Use of a segmented detector allows for the event-by-event correction of the light yield non-proportionality and for the preferential selection of events with near-complete energy deposition, since these will typically have high segment multiplicities.Comment: Accepted for publication in Nuclear Instruments and Methods in Physics Research Section

DIS-PIPE: A Tool for Data Pipeline Discovery

This paper presents DIS-PIPE, a software tool that leverages well-established process mining techniques to tackle the Data Pipeline Discovery (DPD) task. Data pipelines are composite steps that move data from disparate sources to some data consumers. While data travels through the pipeline, it can undergo various transformations processed by computational platforms. In this context, DPD targets learning the structure and behavior of a data pipeline from an event log that keeps track of its past executions, uncovering, to some extent, specific execution-related dark data whose knowledge is critical to improving the quality of pipeline modeling. DIS-PIPE has been designed, implemented, and validated in the H2020 European project DataCloud context, and is able to interpret XES logs enriched with information to capture the core concepts of data pipelines

A mobile antineutrino detector with plastic scintillators

We propose a new type segmented antineutrino detector made of plastic scintillators for the nuclear safeguard application. A small prototype was built and tested to measure background events. A satisfactory unmanned field operation of the detector system was demonstrated. Besides, a detailed Monte Carlo simulation code was developed to estimate the antineutrino detection efficiency of the detector.Comment: 23 pages, 11 figures; accepted for publication in Nuclear Instruments and Methods in Physics Research

An End-To-End Execution of a Logistic Process in an AI-Augmented Business Process Management System

This paper presents an end-to-end execution of a real-world business process (BP) in the logistics domain to illustrate how an AI-Augmented Business Process Management System (ABPMS) can increase BP automation compared to a traditional BPMS. In addition, we discuss concrete AI-based solutions for the implementation of the ABPMS lifecycle stages

Automated Generation of Executable RPA Scripts from User Interface Logs

Robotic Process Automation (RPA) operates on the user interface (UI) of software applications and automates - by means of a software (SW) robot - mouse and keyboard interactions to remove intensive routine tasks (or simply routines). With the recent advances in Artificial Intelligence, the automation of routines is expected to undergo a radical transformation. Nonetheless, to date, the RPA tools available in the market are not able to automatically learn to automate such routines, thus requiring the support of skilled human experts that observe and interpret how routines are executed on the UIs of the applications. Being the current practice time-consuming and error-prone, in this paper we present SmartRPA, a cross-platform tool that tackles such issues by exploiting UI logs to automatically generate executable RPA scripts that automate the routines enactment by SW robots

Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments

This study contributes to understanding semi-automated ergonomic risk assessments in industrial manufacturing environments, proposing a practical tool for enhancing worker safety and operational efficiency. In the Industry 5.0 era, the human-centric approach in manufacturing is crucial, especially considering the aging workforce and the dynamic nature of the entire modern industrial sector, today integrating digital technology, automation, and sustainable practices to enhance productivity and environmental responsibility. This approach aims to adapt work conditions to individual capabilities, addressing the high incidence of work-related musculoskeletal disorders (MSDs). The traditional, subjective methods of ergonomic assessment are inadequate for dynamic settings, highlighting the need for affordable, automatic tools for continuous monitoring of workers’ postures to evaluate ergonomic risks effectively during tasks. To enable this perspective, 2D RGB Motion Capture (MoCap) systems based on computer vision currently seem the technologies of choice, given their low intrusiveness, cost, and implementation effort. However, the reliability and applicability of these systems in the dynamic and varied manufacturing environment remain uncertain. This research benchmarks various literature proposed MoCap tools and examines the viability of MoCap systems for ergonomic risk assessments in Industry 5.0 by exploiting one of the benchmarked semi-automated, low-cost and non-intrusive 2D RGB MoCap system, capable of continuously monitoring and analysing workers’ postures. By conducting experiments across varied manufacturing environments, this research evaluates the system’s effectiveness in assessing ergonomic risks and its adaptability to different production lines. Results reveal that the accuracy of risk assessments varies by specific environmental conditions and workstation setups. Although these systems are not yet optimized for expert-level risk certification, they offer significant potential for enhancing workplace safety and efficiency by providing continuous posture monitoring. Future improvements could explore advanced computational techniques like machine learning to refine ergonomic assessments further