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Blockchain-Powered NFTs: A Paradigm Shift in Carbon Credit Transactions for Traceability, Transparency, and Accountability 20th

Blockchain-Powered NFTs: A Paradigm Shift in Carbon Credit T...

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20th European, Mediterranean, and Middle Eastern Conference (EMCIS)

作者： Khanna, Abhirup Maheshwari, Piyush Univ Petr & Energy Studies Dehra Dun Uttarakhand India British Univ Dubai BUiD Dubai U Arab Emirates

ISBN: (纸本)9783031564772;9783031564789

The adoption of carbon credit systems has emerged as a pivotal strategy in addressing climate change and promoting sustainable environmental practices. This research paper delves into the multifaceted landscape of carbon credit systems, specifically focusing on the challenges inherent in their design and implementation. We investigate the innovative use of Non-Fungible Tokens (NFTs) as a transformative approach to carbon credits, exploring the intricacies of NFT structures tailored for carbon credit trading. Furthermore, the paper presents a comprehensive examination of the system architecture underpinning a blockchain-enabled NFT trading platform for carbon credits. The architectural framework's design and components are meticulously scrutinized to ensure transparency, security, and efficiency in the trading ecosystem. The research also dissects the intricate functionalities that empower the blockchain-NFT trading system, facilitating seamless transactions and traceability. A pivotal facet of this study is a compelling case study spotlighting the application of a Blockchain-NFT trading system for Agricultural Carbon Credits within a rural farming community. The case study highlights the tangible benefits reaped by farmers through the innovative system, shedding light on how the technology incentivizes sustainable farming practices and offers an additional revenue stream.

关键词： Blockchain NFT Carbon credits Trading platform SDG 13

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Petri Net Classes for Collaboration Mining: Assessment and Design Guidelines 5th

Petri Net Classes for Collaboration Mining: Assessment and D...

引用

5th International Conference on Process Mining (ICPM)

作者： Benzin, Janik-Vasily Rinderle-Ma, Stefanie Tech Univ Munich TUM Sch Computat Informat & Technol Garching Germany

ISBN: (纸本)9783031561061;9783031561078

Collaboration mining develops discovery, conformance checking, and enhancement techniques for collaboration processes. The collaboration process model is key to represent the discovery result. As for process mining in general, Petri Net classes are candidates for collaboration process models due to their analytical power. However, a standard model class to represent collaboration processes is lacking due to the heterogeneity of collaboration and, thus, of collaboration mining techniques. Collaboration heterogeneity requires to cover, for example, intra-organizational collaborations as well as choreographies that span a process across multiple organizations. A standard collaboration model class would advance collaboration mining by focusing discovery through a common target model, supporting comparison, and enabling flexible mining pipelines. To find a standard model class, we aim at capturing collaboration heterogeneity in a meta model, assess Petri net classes as candidates for collaboration process models through the meta model, and derive design guidelines for the collaboration discovery.

关键词： Collaboration Mining Collaboration Process Models Petri Net Classes Design Guidelines

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Customizing a Generic Digital Transformation Objectives Model onto a Telecommunication Company 1

引用

36th International Conference on Advanced information Systems Engineering (CAiSE)

作者： Aghakhani, Ghazaleh Xu, Ke Wautelet, Yves Tsilionis, Konstantinos Kolp, Manuel Catholic Univ Louvain Louvain La Neuve Belgium KULeuven Leuven Belgium Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (数字)9783031610035

ISBN: (纸本)9783031610028;9783031610035

Setting-up a Digital Transformation (DT) strategy appears to be challenging for a lot of organizations. Indeed, C-level managers often do not know how to coordinate and align each individual initiative towards a well established long-term vision. Previous work allowed to build-up a set of DT strategic objectives out of literature, validate and extend them through expert opinions. The refined representation was then customized/instantiated onto a case in the healthcare sector. Further validation is nevertheless needed in order to understand the applicability of the generic DT strategic objectives onto cases in various sectors and, very importantly, determine the pros and the cons of such an approach. This paper is part of this effort. It depicts the application of the generic DT strategic objectives model onto the case of a telecommunication company and the validation of the instance. information collection and validation are performed through domain experts interviews. We discovered that the instantiated strategic DT objectives representation helps to clearly identify the priorities that should be considered by the company in formulating the organization's DT strategy. These prioritized objectives entail the analysis of the government policies and the adjustment of human resources management. The most frequent critics from the three interviewees consist in the generic model's failure in recognizing the vital stake that the government has in the management of a state-owned company. Finally, some objectives were not fully comprehended by the managers despite our detailed explanation, such as the financial allocation ability which, to a certain extent, entails the meaning of assessment of impacts on current core businesses.

关键词： Digital Transformation Digital Transformation Strategy Digital Transformation Objectives

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Analysis of Fuzzy Techniques in Edge Detection 25th

Analysis of Fuzzy Techniques in Edge Detection

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25th International Conference on Enterprise information Systems (ICEIS)

作者： Silva, Miqueias Amorim Santos Dimuro, Gracaliz P. Borges, Eduardo N. Lucca, Giancarlo Marco-Detchart, Cedric Univ Fed Rio Grande Ctr Computat Sci Rio Grande RS Brazil Univ Politecn Valencia Valencia Spain

ISBN: (纸本)9783031647475;9783031647482

Edge detection, which identifies the utmost boundary between two areas with distinct characteristics, is a foundational challenge in computer vision. The ambiguity inherent in this detection-like the blurred transition zone due to image discretization or factors like noise and varying lighting-warrants an approach grounded in fuzzy logic theory. This study offers a comprehensive review of edge detection methods that utilize fuzzy logic, drawing from two academic databases: Scopus and Web Of Science. Out of the systematic literature review, 34 papers were chosen, and their methodologies were outlined in this investigation. The review revealed that in recent times, fuzzy logic has been integrated with other methods to enhance the efficiency of existing techniques or to minimize computational demands. Research using advanced interval fuzzy logic has been adopted for its superior adaptability in addressing the uncertainties inherent in edge detection.

关键词： Edge detection Fuzzy methods Systematic literature review

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A Chatbot Generator for Improved Digital Governance 20th

A Chatbot Generator for Improved Digital Governance

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20th European, Mediterranean, and Middle Eastern Conference (EMCIS)

作者： Bouras, Christos Diasakos, Damianos Katsigiannis, Chrysostomos Kokkinos, Vasileios Gkamas, Apostolos Karacapilidis, Nikos Charalabidis, Yannis Lachana, Zoi Alexopoulos, Charalampos Papadopoulos, Theodoros Karamanolis, Georgios Psalidas, Michail Univ Patras Comp Engn & Informat Dept Patras 26504 Greece Univ Ecclesiast Acad Athens Athens 14561 Greece Univ Patras Dept Mech Engn Patras 26504 Greece Univ Aegean Dept Informat & Commun Syst Engn Samos 83200 Greece Crowd Policy Athens 18345 Greece

ISBN: (纸本)9783031564772;9783031564789

Chatbots, the pioneering conversational artificial intelligence (AI) agents, have experienced remarkable growth and integration in various domains. In modern societies, chatbots have emerged as transformative digital entities, revolutionizing the way humans interact with technology. These conversational AI agents have transcended their initial applications to become integral parts of various industries and daily life. One of the most prominent roles of chatbots is in customer service, where they offer round-the-clock assistance, swift issue resolution, and personalized interactions. By handling routine queries and tasks, chatbots free up human agents to focus on complex and specialized issues, thus optimizing overall efficiency and customer satisfaction. To this end, this paper aims to present and describe the architecture of a novel chatbot generator with improved functionality in terms of quality of communication with end users and level of provided services, with a specialized infrastructure understanding the Greek language. The chatbot generator was developed in the framework of a research project and will be pilot tested by two end-users, the National Bank of Greece (NBG) and the General Secretariat for information Systems & Digital Governance (GSIS-DG).

关键词： Artificial Intelligence Natural Language processing Machine Learning Virtual Assistants

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Sparse Mixtures of Shallow Linear Experts for Interpretable and Fast Outcome Prediction 5th

Sparse Mixtures of Shallow Linear Experts for Interpretable ...

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5th International Conference on Process Mining (ICPM)

作者： Folino, Francesco Pontieri, Luigi Sabatino, Pietro Inst High Performance Comp & Networking ICAR CNR Via P Bucci 8-9C I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9783031561061;9783031561078

(Process) Outcome Prediction entails predicting a discrete property of an unfinished process instance from its partial trace. Various outcome predictors discovered via Machine Learning (ML) methods, like rule/tree ensembles and (deep) neural networks, have achieved top accuracy performances. However, their opaqueness makes them unsuitable for scenarios necessitating understandable outcome predictors. Aligning with recent efforts to mine inherently interpretable predictors, we suggest training a sparse Mixture-of-Experts, with the "gate" and "expert" sub-nets being Logistic Regressors. This ensemble of specialized predictors is trained in a end-to-end way while restricting the number of input features used in the sub-nets, as an alternative to typical multi-step/objective mining pipelines (including, e.g., a global feature selection step followed by an ML one). This enables different experts to focus on varied input features for predicting the outcomes of instances in their competency regions. Test results on benchmark logs confirmed the ability of this approach to reach a compelling trade-off between accuracy and interpretability compared to existing solutions.

关键词： Process Mining Machine Learning XAI Green AI

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Pricing4SaaS: Towards a Pricing Model to Drive the Operation of SaaS 36th

Pricing4SaaS: Towards a Pricing Model to Drive the Operation...

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36th International Conference on Advanced information Systems Engineering (CAiSE)

作者： Garcia-Fernandez, Alejandro Parejo, Jose Antonio Ruiz-Cortes, Antonio Univ Seville Inst I3US SCORE Lab Seville Spain

ISBN: (纸本)9783031609992;9783031610004

The Software as a Service (SaaS) model is a distribution and licensing model that leverages pricing structures and subscriptions to profit. The utilization of such structures allows information Systems (IS) to meet a diverse range of client needs, while offering improved flexibility and scalability. However, they increase the complexity of variability management, as pricings are influenced by business factors, like strategic decisions, market trends or technological advancements. In pursuit of realizing the vision of pricing-driven IS engineering, this paper introduces Pricing4SaaS as a first step, a generalized specification model for the pricing structures of systems that apply the Software as a Service (SaaS) licensing model. With its proven expressiveness, demonstrated through the representation of 16 distinct popular SaaS systems, Pricing4SaaS aims to become the cornerstone of pricing-driven IS engineering.

关键词： Cloud-based IS engineering Pricing Software as a Service

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Which Legal Requirements are Relevant to a business Process? Comparing AI-Driven Methods as Expert Aid 18th

Which Legal Requirements are Relevant to a Business Process?...

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18th International Conference on Research Challenges in information Sciences (RCIS)

作者： Sai, Catherine Sadiq, Shazia Han, Lei Demartini, Gianluca Rinderle-Ma, Stefanie Tech Univ Munich TUM Sch Computat Informat & Technol Garching Germany Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia

ISBN: (纸本)9783031594649;9783031594656

Organizations are obliged to ensure compliance with an increasing amount of regulatory requirements stemming from laws, regulations, directives, and policies. As a first step, it is to be determined which of the requirements are relevant in a certain context, depending on factors such as location of the organization and the business processes. For the processes, the identification of relevant requirements can be detailed by an assessment of which parts of each document are relevant for which step of a given process. Nowadays the identification of process-relevant regulatory requirements is mostly done manually by domain and legal experts, posing a tremendous workload due to the extensive number of regulatory documents and their frequent changes. Hence, this work examines how organizations can be assisted in the identification of relevant requirements for their processes based on embedding-based NLP ranking and generative AI. The evaluation highlights strengths and weaknesses of both methods regarding applicability, automation, transparency, and reproducibility. The evaluation results lead to guidelines on which method combinations will maximize benefits for given characteristics such as process usage, impact, and dynamics of an application scenario.

关键词： business Process Compliance Regulatory Relevance Legal information Retrieval Large Language Models

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Parallelism-Based Session Creation to Identify High-Level Activities in Event Log Abstraction 5th

Parallelism-Based Session Creation to Identify High-Level Ac...

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5th International Conference on Process Mining (ICPM)

作者： Dogan, Onur De Leoni, Massimiliano Univ Padua Dept Math I-35121 Padua Italy Izmir Bakircay Univ Dept Management Informat Syst TR-35665 Izmir Turkiye

ISBN: (纸本)9783031561061;9783031561078

Process mining utilizes event data to gain insights into the execution of processes. While techniques are valuable, their effectiveness may be hindered when dealing with highly complex processes that have a vast number of variants. Additionally, because the recorded events in information systems are at a low-level, process mining techniques may not align with the higher-level concepts understood at the business level. Without abstracting event sequences to higher-level concepts, the outcomes of process mining, such as discovering a model, can become overly complex and challenging to interpret, rendering them less useful. Some research has been conducted on event abstraction, often requiring significant domain knowledge that may not be readily accessible. Alternatively, unsupervised abstraction techniques may yield less accurate results and rely on stronger assumptions. This paper introduces a technique that addresses the challenge of limited domain knowledge by utilizing a straightforward approach. The technique involves dividing traces into batch sessions, taking into account relationships between subsequent events. Each session is then abstracted as a single high-level activity execution. This abstraction process utilizes a combination of automatic clustering and visualization methods. The proposed technique was evaluated using a randomly generated process model with high variability. The results demonstrate the significant advantages of the proposed abstraction in effectively communicating accurate knowledge to stakeholders.

关键词： Event log abstraction Parallel relationships Clustering Visual analytics Process discovery

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Industrial Object Detection: Leveraging Synthetic Data for Training Deep Learning Models 11th

Industrial Object Detection: Leveraging Synthetic Data for T...

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11th International Conference on Industrial Engineering and Applications - Europe (ICIEA - Europe)

作者： Ouarab, Sarah Boutteau, Remi Romeo, Katerine Lecomte, Christele Laignel, Aristid Ragot, Nicolas Duval, Fabrice Univ Rouen Normandie Univ Le Havre Normandie Normandie Univ INSA Rouen NormandieLITIS UR 4108 F-76000 Rouen France LINEACT CESI Rouen France LINEACT CESI Lyon France

ISBN: (纸本)9783031581120;9783031581137

The increasing use of synthetic training data has emerged as a promising solution in various domains due to its ability to provide accurately labelled datasets at a lower cost compared to manually annotated real-world data. In this study, we investigate the use of synthetic data for training deep learning models in the field of industrial object recognition. Our goal is to evaluate the performance of different models trained with varying ratios of real and synthetic data, with the aim of identifying the optimal ratio that yields superior results. In addition, we investigate the impact of introducing randomisation into the synthetic data on the overall performance of the trained models. The results of our research contribute to the understanding of the role of synthetic data in industrial object detection.

关键词： Synthetic data Deep Learning Detection models Industrial Objects

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