robotics process automation

This comprehensive article explores the critical challenge of securing Continuous Integration and Continuous Deployment (CI/CD) pipelines in modern software development. It addresses the common security threats faced by organizations, including credential leaks, supply chain attacks, and unauthorized access, while offering actionable strategies to mitigate these risks.

Cloud data pipelines need better monitoring—traditional threshold-based systems
often miss subtle anomalies or flood teams with false alarms. But can AI do better? This
study compares hybrid machine learning (ML) models against standard threshold
monitoring in Azure Data Factory (ADF), testing whether combining Azure’s Anomaly
Detector with custom LSTM neural networks improves detection speed, accuracy, and
efficiency.
Using real-world enterprise data, we measure:
Detection accuracy (precision, recall) for sudden spikes, slow drifts, and tricky
contextual anomalies.
Alert speed—how much faster AI spots issues vs. threshold rules in streaming/batch
workloads.
Compute trade-offs—does the extra ML overhead pay off in high-volume pipelines?
Can AI Outsmart Threshold Alerts? A Hybrid Machine Learning Approach for Smarter Anomaly Detection in
Azure Data Pipelines
Results show the ensemble model cuts false alerts by 32% and detects anomalies
47% earlier, adding under 500ms latency—viable for real-time use. But there’s a catch:
AI’s "black box" decisions complicate compliance. We break down the pros/cons for
Azure teams, balancing cost, complexity, and explainability when shifting from rules to
AI.

The foreseeable future will likely see a profound impact caused by the advancing technology of Artificial Intelligence systems, in our day-to-day activities and routines, just like the same effect brought about by the introduction of the internet and World Wide Web [1-2]. The AIs currently in use, and the coming future of general intelligence machine systems, demand and expect appropriate – or even the best possible – Governance and Compliance Laws to be established to avert any possible negative consequences that could arise from their interaction with humanity, nature, the economy, governments, and society [2-4]. In other words, they need to answer two vital questions: who governs whom? And who governs what? That said, the question of Governance indeed takes centre stage.

International labor mobility and unemployment are two faces of the coins and they comprehensively affect the economic growth so that this study deals with the analysis to check the effect of international labor mobility and unemployment at economic growth. 1992 to 2017 data is collected with 5 years lap from United States, Australia, Canada, Switzerland, Germany, France, and the United Kingdom. Purposive and Convenient sampling technique used for data collection. The implication for the countries policy makers and departments related to immigration. Study adhere to the limitations of sample size which can be increased for further study.

International labor mobility and unemployment are two faces of the coins and they comprehensively affect the economic growth so that this study deals with the analysis to check the effect of international labor mobility and unemployment at economic growth. 1992 to 2017 data is collected with 5 years lap from United States, Australia, Canada, Switzerland, Germany, France, and the United Kingdom. Purposive and Convenient sampling technique used for data collection. The implication for the countries policy makers and departments related to immigration. Study adhere to the limitations of sample size which can be increased for further study.

Advancements in group insurance solutions have long struggled to match the ever-changing expectations of insureds in the face of emerging risks, evolving technology, demands for immediacy, and the ever-increasing use of personal data. Although change is felt at all layers of the information technology ecosystem associated with group insurance, solutions have only cursorily benefited from advanced automation and enhanced analytic insights. We argue that the demands from insureds and the accompanying change drivers can only be addressed through AI-enhanced technology architectures, with a multi-layered view of the group insurance information technology ecosystem. At the core of an architecture that delivers the necessary level of adaptability and flexibility as well as the use of extensive internal and external data resources to uncover and characterize the complex relationships among risk frameworks, technology capabilities, and product and data designs is a hierarchical knowledge graph. Supported by a technology mix that utilizes cloud-based microservices and applications as well as distributed ledger marts, knowledge graphs hold the key to achieve the necessary level of speed, product design, technology integration, process automation, decision making, and customer trust in a rapidly evolving, disruptive, and competitive marketplace focused on group insurance. The work we present aims to do three things. Firstly, we describe the challenges group insurance faces well into the future from the twin perspective of insureds and the marketplace. Secondly, we bridge the demands and the necessary as composite solutions that are AI-enhanced, through guiding technology and data architectures, that address the analytics, automation, and IT complexity at scale. Thirdly, upon the culmination of these solutions through multi-layered architectures with knowledge graphs at the core along with a curated AI technology stack, we sketch out potential resultant group insurance products and services forms.

Rapid advances in technology are causing finance departments to examine the kind of digital transformation they should undergo in the future. For many finance departments, efficiency may be a concern. However, for many more, failing to make such transformations proactively presents existential threats. Facing change requires a complete examination of practices, technologies, and systems in terms of cloud-native architecture and AI completion. Transformation of the data ecosystem may require considerable legacy overhaul. This means that consolidation and knowledge management processes may be particularly important. By understanding the architecture and deliverability of data solutions, the information will be readily available with the right tools. AI completion will provide early assistance around knowledge gaps and quick time to implementation on repeatable information. Together, these two components offer the opportunity to implement an AI ecosystem that is actually actionable and a foundation for future growth. Change means different things to different people across the organization. In parts, particularly for finance departments, AI completion may take the form of a considerable number of assistive technologies, covering simple decision chain assistance all the way up to full task automation. Some of these technologies have existed for many years, and they require considerable finance transformation effort around data discipline before they can be leveraged. Other parts may be completely new, offering technologies that were unimaginable to finance departments only two years ago. Each zero and one has its own value chain within an organization. Value accumulation will be challenged, or even blocked, from being realized. In the absence of a comprehensive cloud-native architecture for all of the organization's data and information assets, many technologies Deep Science Publishing

AI is revolutionizing several sectors, influencing highly specialized areas such as tax regulations and consulting. By fully enhancing tax compliance, tax optimization can be achieved, as all data is seamlessly available in real time. This can be done without excess costs via in-house solutions. Tax compliance is relevant for all sectors of an economy. Because of the relatively high complexity of tax issues insurance companies are facing, compliance is not always easily achieved. Due to real-time compliance advantages AIassisted software technology might help insurance companies predominantly in terms of foreign withholding tax and insurance premium tax. However, the need for state-of-theart tax solutions might also lead to specialized tax consulting, withholding, and payment service solutions . The compliance risk of insurance companies is predominantly relevant for foreign withholdings tax. Insurance companies enable private individuals and corporations to financially mitigate risks such as fire damage, burglary, environmental damage, personal accident, illness or death, company bankruptcy, and natural disasters. In return, they continuously receive premiums, i.e. an amount of money that corresponds to the sum of possible insured losses adjusted for the risk of the insured event occurring. Potential tax issues arise when excess funds are generated, which lead to the allocation to general reserves. For insurance companies with international exposure, the general reserves can lead to substantial foreign withholding tax burdens. Because insurance contracts are concluded over several years, the business activities of the insurance holder can be subject to taxation in different countries during the term of the contract. If the business activities are subject to taxation in different countries, foreign withholding tax might arise if the excess funds are transferred to the corporate holder's home jurisdiction at the Deep Science Publishing

The development of Robotic Process Automation (RPA) has altered financial management by automating repetitive activities, reducing errors, and providing real-time reports. Objective: To assess the effectiveness and scalability of RPA integration in cost accounting and financial systems. Methods: A structured implementation approach occurred, involving process identification, workflow design, RPA creation, and performance evaluation. Results: RPA lowered processing time by 95%, increased cost allocation accuracy to 99.5%, and reduced errors by 95%. Conclusion: RPA integration improves operational efficiency and financial correctness, demonstrating its ability to provide scalable and error-free financial optimisation.

This comprehensive technical article explores the transformative impact of artificial intelligence on insurance underwriting processes. The article examines how AI technologies are revolutionizing traditional underwriting methods through advanced data processing, machine learning architectures, and automated systems. The article

AI/ML technologies are now maturing, available in performance and price, and secured as well as enabled by evolving and well-accepted cloud environments based on shared or hybrid infrastructures. AI/ML and advanced analytics are rapidly introducing new capabilities in banking as part of the digital transformation of traditional IT systems. On the one hand, IT systems traditionally have been a siloed set of specific applications, but during the last 50 years the R&D efforts were often focused on coping with the increasing complexity of legacy systems, accomplishing virtualization, and ensuring security and reliability. On the other hand, in recent years the emergence of new methodologies and new technologies has generated the possibility of advancing the functionality of already available systems. The first products of this opportunity have been realized as AI/ML systems that provide online deployment of pretrained models. While the cost could be high, especially in the case of generating the training data set, for many use cases it could be well worth the investment. However, generative AI and other new capabilities are now challenging the banks in providing sufficient robustness and reliability in the production environment of on-premise systems. The speed and applicability of these newer technologies are foreseen by the R&D department of banks that made the necessary investment into the knowledge and resource foundation.

The rapid adoption of automated accounting systems has transformed the accounting profession, reshaped traditional roles and introducing new opportunities and challenges. This study explores the impact of automation on accounting practices, focusing on how it has redefined the role of the accountant. Automation, driven by technologies such as robotic process automation (RPA), artificial intelligence (AI), and cloud-based tools, has alleviated manual, repetitive tasks, enabling accountants to focus on higher-level functions like data analysis, strategic decision-making, and advisory services. While automation offers significant advantages in terms of efficiency, accuracy, and real-time data processing, it also brings challenges, including concerns over job displacement, data security, and the need for new skill sets. The research discusses how the profession is evolving from a traditional role focused on bookkeeping and compliance to one centered on data-driven decision-making and organizational strategy. The study further examines the implications of these changes for accounting professionals, organizations, and educational institutions. Recommendations are provided for policymakers, educators, and accounting professionals on how to navigate the challenges and opportunities presented by automation. This paper contributes to the growing body of literature on accounting technology and provides a roadmap for the future of the profession in an increasingly automated world.

Pega has emerged as a key enabler of hyperautomation, integrating robotic process automation (RPA), artificial intelligence (AI), and advanced workflow management to drive digital transformation across enterprises. This article explores Pega's strategic approach to automating complex business processes, emphasizing its ability to seamlessly orchestrate collaboration between humans and machines. By leveraging data-driven insights and realtime analytics, Pega empowers organizations to eliminate silos, enhance operational agility, and significantly boost productivity. The article highlights key case studies and industry examples, demonstrating how Pega's platform not only streamlines routine tasks but also fosters intelligent decision-making, enabling sustainable digital transformation. Through a detailed analysis of Pega's technical architecture, implementation strategies, and performance outcomes, this research provides actionable insights for organizations seeking to harness the power of hyperautomation. The findings underscore Pega's leadership in driving innovation and its pivotal role in shaping the future of enterprise automation.

ontologies dealing with processes or scenarios will have something corresponding to events. PSL uses the term "activity_occurrence " for events. Cyc uses the term "Event". Other possible terms have drawbacks. "Action " connotes an event that has an agent, so it is only a subclass of events. "Process " and "Procedure" connote complex events that have subevents, and we need a term that will cover primitive events as well. One can argue that "activity" and "activity occurrence " have the same problem. For these reasons, we have chosen the term "Events". Types and Tokens We can refer to both event types and event tokens. PSL uses the terms "activity " and "activity_occurrence " for these two. Cyc uses the terms "EventType " and "Event". Another possible pair of term is "EventClass " and "EventInstance", but this biases one toward interpretation of t...

This paper explores how Robotic Process Automation (RPA) and Artificial Intelligence (AI) are reshaping the financial services industry into a dynamic and intelligent ecosystem. Drawing upon pre-2023 academic and industry studies, the research investigates the integration of cognitive automation, machine learning, and intelligent bots in streamlining financial workflows, mitigating fraud, improving customer experience, and enhancing compliance. Through a multi-dimensional analysis of architecture, implementation frameworks, real-world use cases, and ethical considerations, this paper provides a forward-looking roadmap toward smart finance. Strategic visuals, system models, and KPIs support the claim that automation is no longer optional but a foundational pillar of future-ready financial systems.

The evolution of banking from traditional brick-and-mortar structures to digital and AI-driven services has transformed the way
financial transactions are conducted. One of the latest advancements in banking technology is the adoption of AI-powered chatbots,
which enable seamless interaction between customers and banking systems. These chatbots function as virtual assistants, providing
instant responses to customer queries, assisting with banking transactions, and enhancing customer relationship management
(CRM).The adoption of AI chatbots in the banking sector has accelerated with advancements in artificial intelligence and machine
learning.

Robotic Process Automation (RPA) allow people focus on meaningful tasks rather than transferrable recurring and non-value-added ones. Whereas it provides an opportunity to foster agility and productivity, RPA is also perceived to put accountants at risk as many repetitive processes clinging to the profession can be substituted by the technology. As there is little study on how RPA is immersed into play this research aims to fill this spot by designing RPA solution for typical accounting taks in telecommunication entity. Design science methodology (DSR) was employed to guide the process of artifact development. Building on the initial as-is process model, RPA-oriented process model was then produced and then processed according to DSR method into an instantiation. The accounting task design result shows substantive progress in average throughput time from pre-RPA task 02:41 minutes to post-RPA of 19 seconds with no error conceded, thus creating 7 times faster processing. The current ...

Receipt preparing is one of the key back-office activities that requests a great deal of manual exertion. With manual cycles set up, organizations sit around idly acquiring, allotting and affirming Receipts. (RPA) Robotic Process Automation is a more effective approach to Process invoicing. In this project we are going to Extract Invoice data and then filter them according to their due date and put those invoices into their respective excel sheets. Further the Excel sheet with expired Due Date will be sent to Respective Email Address. This Invoice processing Automation System is ,also trained to understand real world documents. It can also identify and extract a wide range of business values from your receipts and invoices, depending on the needs and requirements of your accounts payable and expense compliance processes. This Automation System will Process Invoices in Less time and with no Errors. This Automation Sem can Process More than 100's of Invoices Per day which is very hard to get processed manually. It will save Human Efforts, it will give the company a monetary gain with minimum errors.

Background: The industrialization process has led to the automation of business processes with the goal of economic efficiency. This research was conducted at a trading company that requires an effective administrative process with time efficiency. So this need becomes a reason for the need for technology. Currently, a new technology has emerged that will revolutionize how humans work in companies, namely Robotic Process Automation. Purpose: This study focuses on first, proposing a model in the scope of financial domain, especially in invoice processing and reconciliation. Second, proposing an RPA solution using UI Path which can be used to improve invoice processing and reconciliation processes. Design: This study uses the Design Science Research (DSR) methodology, so that the research object is the framework of the design itself. There are several steps taken to build RPA, namely (1) Problem Analysis and Motivation, (2) Determining Solution Goals, (3) Designing and Developing Solutions (4) performance evaluation. From the research, the results show that RPA performance has met expectations in terms of: speed. Finding/ Result: The findings of this study indicate that there are limitations in implementation due to variations in invoice formats across different companies. As a result, there is a need for ongoing adjustments and regular updates to the system and hardware to accommodate changes in invoice formats and business processes of each company. Conclusion: According to the study's findings it was concluded that RPA model has a good performance and has already met the side's expectations in terms of speed. Originality/ value (state of the art): The use of Robotic Process Automation (RPA) in financial processes, notably invoice data extraction, is a cutting-edge technology that improves financial efficiency and accuracy. This study, conducted on an Indonesian trading organization, reveals how RPA may be used to streamline financial workflows by automating repetitive, time-consuming procedures, minimizing human error, and enhancing data management processes.

A dynamic and complex process environment forces the automated execution and monitoring of processes to face a lot of hard problems. This paper shows how the execution of agile workflows can be assisted by AI planning techniques. In contrast to previous approaches, this method allows for an online adaption of simple process models to changes in the process environment. It can handle a manifold of unexpected events and guarantees the soundness and completeness of the adapted workflow.

In this paper, a process workflow for Bot is created using robotic process
automation (RPA), associated with artificial intelligence that is used to
stream line the administrative tasks and alleviate stress levels of faculty in
handling administrative tasks while teaching in higher education. These
activities are must for National Academic Audit Council (NAAC)
accreditation and All India Survey on Higher Education (AISHE) surveys,
which strive to bring quality in teaching higher education by shaping
educational policies. Therefore, ensuring the accuracy of this data is
paramount to avoid misleading decisions. The Bot automatically gathers
student results from the website and saves them into individual files,
eliminating the need for human intervention. It is trained to find the related
file of student and update his results of upcoming semesters or backlogs. The Bot efficiently manages folders during file saving to enhance retrieval.
Additionally, it maintains pertinent student details such as community, caste, and religion, beneficial for educational policy surveys aiming for improved quality. Moreover, it generates and updates reports post each process execution, ensuring data integrity, and can be trained for statistical analysis to predict student outco

User interaction logs allow us to analyze the execution of tasks in a business process at a finer level of granularity than event logs extracted from enterprise systems. The fine-grained nature of user interaction logs open up a number of use cases. For example, by analyzing such logs, we can identify best practices for executing a given task in a process, or we can elicit differences in performance between workers or between teams. Furthermore, user interaction logs allow us to discover repetitive and automatable routines that occur during the execution of one or more tasks in a process. Along this line, this chapter introduces a family of techniques, called Robotic Process Mining (RPM), which allow us to discover repetitive routines that can be automated using robotic process automation technology. The chapter presents a structured landscape of concepts and techniques for RPM, including techniques for user interaction log preprocessing, techniques for discovering frequent routines, notions of routine automatability, as well as techniques for synthesizing executable routine specifications for robotic process automation.

Natural language processing (NLP) is a research field that provides huge potential to automate accounting tasks dealing with text data. This research studies the application of NLP in automatically categorizing invoices based on the invoice text description. The study employs semantic enrichment, data augmentation, and deep learning to address the NLP unique issues posed by the inherent short text and multiclass imbalance nature of invoice descriptions. Semantic enrichment was done using labels as an information source. Training data was artificially increased with either WordNet synonym replacement, Global Vectors for Word Representation (GloVe) word replacement, or Bidirectional Encoder Representations from Transformers (BERT) word replacement method. Each training dataset was then supplied for training with one nondeep learning classifier and two deep learning classifiers respectively, namely Linear Support Vector Machine (LSVM), Bidirectional Long Short-Term Memory (Bi-LSTM), and BERT. Overall, the semantically enriched, WordNet augmented training set paired with the BERT classifier yielded the best results, successfully preserving semantics, reducing noise and overfitting while improving accuracy per class, achieving an increase of performance up to 20 ppts for macro F1 score and 6.7 percentage points (ppts) for accuracy.

In the rapidly evolving realm of software development, Robotic Process Automation (RPA) has emerged as a pivotal technology that holds the potential to redefine the landscape of automation testing. This scholarly article delves into the integration of RPA within the realm of automation testing, elucidating its capacity to augment efficiency and precision. The primary aim of this investigation is to evaluate the efficacy of RPA tools in automating repetitive and time-consuming testing procedures that have traditionally been conducted by human testers. Through a comparative analysis of diverse RPA tools and a series of empirical case studies, we substantiate how RPA can substantially diminish manual exertion while concurrently expanding coverage and dependability in test scenarios. Our findings reveal that RPA, when effectively implemented, not only expedites the testing cycle but also contributes to the production of more robust and dependable software products. The article concludes by deliberating on the broader implications of these findings for the field of software testing, with an emphasis on the necessity for organizations to adapt to RPA-driven methodologies. This scholarly investigation lays the groundwork for future research endeavors aimed at optimizing RPA strategies and their integration with other emerging technologies in the realm of automation testing.

Intelligent Process Automation (IPA) is emerging as a sub-field of AI to support the automation of long-tail processes which requires the coordination of tasks across different systems. So far, the field of IPA has been largely driven by systems and use cases, lacking a more formal definition of the task and its assessment. This paper aims to address this gap by providing a formalisation of IPA and by proposing specific metrics to support the empirical evaluation of IPA systems. This work also compares and contrasts IPA against related tasks such as end-user programming and program synthesis.

The complexity and rigidity of legacy applications in large organizations engender situations where workers need to perform repetitive routines to transfer data from one application to another via their user interfaces, e.g. moving data from a spreadsheet to a Web application or vice-versa. Discovering and automating such routines can help to eliminate tedious work, reduce cycle times, and improve data quality. Advances in Robotic Process Automation (RPA) technology make it possible to automate such routines, but not to discover them in the first place. This paper presents a method to analyse user interactions in order to discover routines that are fully deterministic and thus amenable to automation. The proposed method identifies sequences of actions that are always triggered when a given activation condition holds and such that the parameters of each action can be deterministically derived from data produced by previous actions. To this end, the method combines a technique for compressing a set of sequences into an acyclic automaton, with techniques for rule mining and for discovering data transformations. An initial evaluation shows that the method can discover automatable routines from user interaction logs with acceptable execution times, particularly when there are one-to-one correspondences between parameters of an action and those of previous actions, which is the case of copy-pasting routines.

Natural language processing (NLP) is a research field that provides huge potential to automate accounting tasks dealing with text data. This research studies the application of NLP in automatically categorizing invoices based on the invoice text description. The study employs semantic enrichment, data augmentation, and deep learning to address the NLP unique issues posed by the inherent short text and multiclass imbalance nature of invoice descriptions. Semantic enrichment was done using labels as an information source. Training data was artificially increased with either WordNet synonym replacement, Global Vectors for Word Representation (GloVe) word replacement, or the Bidirectional Encoder Representations from Transformers (BERT) word replacement method. Each training dataset was then supplied for training with one nondeep learning classifier and two deep learning classifiers respectively, namely Linear Support Vector Machine (LSVM), Bidirectional Long Short-Term Memory (Bi-LSTM), and BERT. Overall, the semantically enriched, WordNet augmented training set paired with the BERT classifier yielded the best results, successfully preserving semantics, reducing noise and overfitting while improving accuracy per class, achieving an increase of performance up to 20 percentage points (ppts) for macro F1 score and 6.7 ppts for accuracy.

Robotic Process Automation (RPA) is a new favorite among IT leaders. It can be quickly deployed to automate repetitive tasks, and it saves organizations time and money. RPA bots handle sensitive data, moving it across systems from one process to another. If the data is not secured, it can be exposed and can cost organizations millions of dollars. Security design considerations are vital in RPA implementation for its success. Based on my recent Robotic Process Automation project implementation and research, able to put together the design strategies and best practices to implement security in RPA. In this paper described about RPA, types of Robots and major risks in RPA process and security design considerations in overall RPA process implementations in Organizations. Keywords: Robotic Process Aut

Although the process of digitization in higher education began a long time ago, unfortunately, as various studies have pointed out, many higher education institutions are not yet fully prepared, facing challenges associated with the need for accelerated digitization caused by pandemic. However, the COVID-19 crisis has provided an opportunity to find digital solutions that are inexpensive and can be applied as soon as possible. Leveraging digital development opportunities in education is supported by emerging technologies. In this regard, this paper aims to present how Robotic Process Automation (RPA) technology can help higher education (HE) institutions in their journey of digital transformation, taking into account the different perspectives of teachers and students, researchers, and administrative staff. Although currently there are some studies and scientific papers dealing with the adoption of RPA to solve some of the issues related to higher education and to support teachers, there is little previous research on how RPA-based solutions could help streamline researchers' activities, administrative staff and students. According to a report published by Gartner, Inc. in September 2020, global RPA software revenues are expected to reach $ 1.89 billion in 2021, registering an increase of 19.5% over 2020. Moreover, various researchers estimated that the global RPA market will continue to grow by double digits until 2024. The paper presents some of the main possibilities that open up with the application of such solutions in higher education institutions. Moreover, we present some of the key issues and challenges posed by the adoption of RPA in higher education. In order to ensure the adoption of RPA, several solutions need to be taken into consideration by different higher education stakeholders. For instance, a digital transformation strategy should be developed, to encourage open-mindedness towards automation. Given the potential that RPA offers for higher education institutions, we estimate the intensification of RPA adoption in higher education.

Parallel planning is a paradigm that provides interesting efficiency improvements in the field of classical AI planning and it is one of the key components of successful SAT-based planers. Popularized by the Graphplan algorithm, it provides more structure information about the plan for a plan executor when compared to the traditional sequential plan, which is one of the crucial facts of its usability in real-life scenarios and applications. Our latest research shows that different semantics can be used for parallel planning, while deciding which semantic to use for given application can have significant influence on both practical planning domain modeling and also on the computational efficiency of searching for a plan.

Robotic Process Automation (RPA) is a technology to develop software bots that automate repetitive sequences of interactions between users and software applications (a.k.a. routines). To take full advantage of this technology, organizations need to identify and to scope their routines. This is a challenging endeavor in large organizations, as routines are usually not concentrated in a handful of processes, but rather scattered across the process landscape. Accordingly, the identification of routines from User Interaction (UI) logs has received significant attention. Existing approaches to this problem assume that the UI log is segmented, meaning that it consists of traces of a task that is presupposed to contain one or more routines. However, a UI log usually takes the form of a single unsegmented sequence of events. This paper presents an approach to discover candidate routines from unsegmented UI logs in the presence of noise, i.e. events within or between routine instances that do not belong to any routine. The approach is implemented as an open-source tool and evaluated using synthetic and real-life UI logs.

Mechanical engineering industry has recently been seriously affected by achievements and progress of artificial intelligence and by extremely rapid evolution of Industry 4.0. Considering that the market will rapidly change in the coming decades, the only way to preserve and strengthen market positions for enterprises, is the transformation of production processes within the framework of new technological trends and integrated networked cluster ecosystems.

In the era of technological advancement, there is a need to leverage Digital Transformation (DT) in the field of Human Resource Management. Integration of Robotic Process Automation (RPA) with Human Resource Management (HRM) Systems showcases a multifaceted array of advantages. Firstly, the findings reveal substantial time and cost savings, heightened accuracy in data management, streamlined workflows, and an improvement in overall operational efficiency. Secondly, it also reveals the transformative impact of HRM practices, elucidating the path towards a more agile, tech-driven, and sustainable future for the domain. This study helps to determine the impact of RPA implementation on overall HR effectiveness and the key challenges in the adoption of RPA in HRM practices. The article also talks about the different functional areas where RPA can help automate the business processes and workflow within HRM, including Recruiting, Onboarding, Payroll, Benefits, and Time and absence. The article also outlines how RPA can be leveraged to automate the data upload process typically performed on a frequent basis.

The architecture described in this paper encodes a theory of intentions based on the the key principles of nonprocrastination, persistence, and automatically limiting reasoning to relevant knowledge and observations. The architecture reasons with transition diagrams of any given domain at two different resolutions, with the fine-resolution description defined as a refinement of, and hence tightly-coupled to, a coarse-resolution description. Non-monotonic logical reasoning with the coarse-resolution description computes an activity (i.e., plan) comprising abstract actions for any given goal. Each abstract action is implemented as a sequence of concrete actions by automatically zooming to and reasoning with the part of the fine-resolution transition diagram relevant to the current coarse-resolution transition and the goal. Each concrete action in this sequence is executed using probabilistic models of the uncertainty in sensing and actuation, and the corresponding fine-resolution outcomes are used to infer coarse-resolution observations that are added to the coarse-resolution history. The architecture's capabilities are evaluated in the context of a simulated robot assisting humans in an office domain, on a physical robot (Baxter) manipulating tabletop objects, and on a wheeled robot (Turtlebot) moving objects to particular places or people. The experimental results indicate improvements in reliability and computational efficiency compared with an architecture that does not include the theory of intentions, and an architecture that does not include zooming for fine-resolution reasoning.

The architecture described in this paper encodes a theory of intentions based on the the key principles of non-procrastination, persistence, and automatically limiting reasoning to relevant knowledge and observations. The architecture reasons with transition diagrams of any given domain at two different resolutions, with the fine-resolution description defined as a refinement of, and hence tightly-coupled to, a coarse-resolution description. Non-monotonic logical reasoning with the coarse-resolution description computes an activity (i.e., plan) comprising abstract actions for any given goal. Each abstract action is implemented as a sequence of concrete actions by automatically zooming to and reasoning with the part of the fine-resolution transition diagram relevant to the current coarse-resolution transition and the goal. Each concrete action in this sequence is executed using probabilistic models of the uncertainty in sensing and actuation, and the corresponding fine-resolution out...

In the rapidly evolving realm of software development, Robotic Process Automation (RPA) has emerged as a pivotal technology that holds the potential to redefine the landscape of automation testing. This scholarly article delves into the integration of RPA within the realm of automation testing, elucidating its capacity to augment efficiency and precision. The primary aim of this investigation is to evaluate the efficacy of RPA tools in automating repetitive and time-consuming testing procedures that have traditionally been conducted by human testers. Through a comparative analysis of diverse RPA tools and a series of empirical case studies, we substantiate how RPA can substantially diminish manual exertion while concurrently expanding coverage and dependability in test scenarios. Our findings reveal that RPA, when effectively implemented, not only expedites the testing cycle but also contributes to the production of more robust and dependable software products. The article concludes by deliberating on the broader implications of these findings for the field of software testing, with an emphasis on the necessity for organizations to adapt to RPA-driven methodologies. This scholarly investigation lays the groundwork for future research endeavors aimed at optimizing RPA strategies and their integration with other emerging technologies in the realm of automation testing.

This paper analyzes the rapid onset of Robotic process automation (RPA) technologies in the Czech financial sector between 2015-2020. The development and experience from the "hype-and-fear" phase contributed to business process integration and technological spillovers are expected in the future. If ICT capabilities are the source of performance differences, then most likely candidates are inventory and finances business process integration and implemented Enterprise Resource Planning and Customer Relationship Management systems. The RPA should not be seen only as simple automation but as a complex instrument offering a lot of advantages with a focus on benefits for internal and external stakeholders. PURPOSE. The goal is to qualitatively analyze the experience with RPA implementation and quantitatively assess ICT capabilities via analysis of differences between various organizational ICT activities and types of companies in the Czech financial sector. DESIGN/METHODOLOGY/APPROACH. The qualitative case study was performed in the bank ČSOB, a.s. in 2019, respondents, owners of processes in the final part of automation, were chosen randomly from different departments of the bank. Data for the quantitative part comes from the ICT survey (Czech Statistical Office), business performance dataset (EMIS) and a case study about ICT capability implementation. Differences are assessed and indirectly interpreted using goodness-of-fit approach. FINDINGS AND IMPLICATIONS. The results from the RPA case study revealed that the Czech financial sector is past the "hype and fear" phase and many companies focused on their return on investment and are beginning to focus more on other stakeholders. According to this development, the requirements and outputs are suggested in the phases of RPA implementation. The possible source of profitability performance differences are integrated business processes. LIMITATIONS. Financial sector data are anonymized in ICT surveys and the measurement of the competitive advantage of ICT capabilities is only indirect. Qualitative approach is suggested with focus on technological efficiency measurement using data envelopment approach. ORIGINALITY. This paper provides an understanding of the strong experience in RPA in Czech financial sector. Certain initial setbacks in RPA are expected and this paper suggest to focus on knowledge management (lessons learned) and other requirements influencing the successful RPA prototyping and implementation process.

This paper analyzes the rapid onset of Robotic process automation (RPA) technologies in the Czech financial sector between 2015-2020. The development and experience from the “hype-and-fear” phase contributed to business process integration and technological spillovers are expected in the future. If ICT capabilities are the source of performance differences, then most likely candidates are inventory and finances business process integration and implemented Enterprise Resource Planning and Customer Relationship Management systems. The RPA should not be seen only as simple automation but as a complex instrument offering a lot of advantages with a focus on benefits for internal and external stakeholders. PURPOSE. The goal is to qualitatively analyze the experience with RPA implementation and quantitatively assess ICT capabilities via analysis of differences between various organizational ICT activities and types of companies in the Czech financial sector. DESIGN/METHODOLOGY/APPROACH. Th...

Demo Abstract. Nowadays, the automation of business processes not only spans classical business domains (e.g., banks and governmental agencies), but also new settings such as healthcare, smart manufacturing, domotics and emergency management [2]. Such domains are characterized by the presence of a Cyber-Physical System (CPS) coordinating heterogeneous ICT components with a large variety of architectures, sensors, actuators, computing and communication capabilities, and involving real world entities that perform complex tasks in the “physical” real world to achieve a common goal. In this context, Process Management Systems (PMSs) are used to manage the life cycle of the processes that coordinate the services offered by the CPS to the real world entities, on the basis of the contextual information collected from the specific cyber-physical domain of interest. The physical world, however, is not entirely predictable. CPSs do not necessarily and always operate in a controlled environmen...

Intelligent Process Automation (IPA) is emerging as a sub-field of AI to support the automation of long-tail processes which requires the coordination of tasks across different systems. So far, the field of IPA has been largely driven by systems and use cases, lacking a more formal definition of the task and its assessment. This paper aims to address this gap by providing a formalisation of IPA and by proposing specific metrics to support the empirical evaluation of IPA systems. This work also compares and contrasts IPA against related tasks such as end-user programming and program synthesis.

Automation of Knowledge-Based services is the natural evolution of Robotic Process Automation (RPA), fueled by new technologies, often categorized as Cognitive Automation (CA). The purposes of this research are to (1) explore the Expertise Shared Services and Centers of Expertise functions where automation of knowledge-based processes is most suitable and (2) understand the value drivers and primary tactical challenges in adopting a strategy for automating Knowledge-Based expertise services. In addition to a literature review conducted, we conducted in-depth interviews with selected executives and experts. We developed case studies to better understand how Software Bots can be deployed for automating Knowledge-Based Expertise Services in organizations when they transition to automation of knowledge-driven processes. Results of our research indicate that the majority of executives and experts are aware of the need for automating Knowledge-Based Expertise Services but most remain unclear regarding its value or how to invest in adoption of this new trend.

This article aims to identify the main approach to robotic process automation (RPA) method and trends in the scientific literature in the field of RPA. Moreover, this analysis is needed to build a model of automation of a certain class of business processes. The author focuses on an analysis of publications from 2012 to 2022, collected from the Web of Science, Scopus, and IEEE Xplore databases. The methodology includes source selection, main search limited to RPA, narrowing criteria, adding criteria for subject area, cluster analysis, and limitation to RPA shortcut to achieve most to the point results. The study presents analysis of publication year, type of publication, cluster analysis, as well as the most cited articles. The cluster analysis allowed identification of four primary areas for which publication analysis was conducted. The main value resulting from the creation of this article is the cluster analysis of the most cited publications in the context of RPA seen in the top...

The declarative language Picat has recently entered the scene of constraint logic programming, in particular thanks to the efficiency of its planning library that exploits a clever implementation of tabling, inherithed in part from B-Prolog. Planning benchmarks, used in competitions, are defined in the language PDDL and this implied that Picat users were forced to reimplement those models within the language. In this paper we present an automatic compiler from PDDL to Picat planning models. The compiler is written in Picat and tested on standard PDDL benchmarks.

Robotic Process Automation (RPA) is currently a topic of interest for both research and industry. Many administrative tasks in operations consume a vast amount of time and create little value. The possibility that the responsible employees can quickly and easily automate these processes by themselves makes RPA a promising approach for many processes. However, RPA procedures are only able to automate a process in its present form. In this way, redundancies and excessive process steps are incorporated into RPA process flows. Combining RPA with the popular Business Process Management (BPM) approach poses a useful strategy as the as-is process is optimized first. The RPA procedure is then deployed on the optimized process to reach its full automation potential. This paper proposes a methodology to combine RPA and BPM and demonstrates the potential within a case study. Benefits, limitations, and further research opportunities are likewise addressed.

The declarative language Picat has recently entered the scene of constraint logic programming, in particular thanks to the efficiency of its planning library that exploits a clever implementation of tabling, inherithed in part from B-Prolog. Planning benchmarks, used in competitions, are defined in the language PDDL and this implied that Picat users were forced to reimplement those models within the language. In this paper we present an automatic compiler from PDDL to Picat planning models. The compiler is written in Picat and tested on standard PDDL benchmarks.

Parallel planning is a paradigm that provides interesting efficiency improvements in the field of classical AI planning and it is one of the key components of successful SAT-based planers. Popularized by the Graphplan algorithm, it provides more structure information about the plan for a plan executor when compared to the traditional sequential plan, which is one of the crucial facts of its usability in real-life scenarios and applications. Our latest research shows that different semantics can be used for parallel planning, while deciding which semantic to use for given application can have significant influence on both practical planning domain modeling and also on the computational efficiency of searching for a plan.

Abstract. In this paper we consider processes that run over data stored in a relational database. Our setting is that of ontology-based data access (OBDA), where the information in the database is conceptually represented as an ontology and is declaratively mapped to it through queries. We are interested in verifying temporal logic formulas on the evolution of the information at the conceptual level, taking into account the knowledge present in the ontology, which allows for deducing information that is only implicitly available. ...