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检索条件"丛书名=Lecture Notes in Business Information Processing 252"

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Enabling Interdisciplinary Research in Open Science: Open Science Data Network 1

引用

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

作者： Dang, Vincent-Nam Aussenac-Gilles, Nathalie Megdiche, Imen Ravat, Franck Univ Toulouse Capitole IRIT CNRS UMR 5505 Toulouse France IRIT CNRS UMR 5505 Toulouse France Univ Toulouse ISIS Castres INU Champollion IRITCNRSUMR 5505 Toulouse France

ISBN: (数字)9783031594656

ISBN: (纸本)9783031594649;9783031594656

The aim of Open Science is to open up data to enrich knowledge creation processes. At present, Open Science actors face problems when trying to find and exchange data. Research data management platforms need to address the issue of interoperability to enable interdisciplinary research. Some solutions are available for specific communities, but none addresses the problem as a whole. Based on an extension of the theoretical model of interoperability, which enables us to define the criteria for an information exchange, we have quantitatively evaluated information exchange in Open Science. On the basis of this explorative study, we propose an inter-community and inter-disciplinary information exchange network solution enabling decentralised and federated data management as well as a unified search for datasets across all the entities registered in this network: the Open Science Data Network (OSDN). We carried out a proof of concept to assess the feasibility of the solution. We also evaluated this solution by applying it to a completed agronomy research project. This evaluation enabled us to measure a 7% increase in the volume of data, with an 80% reduction in the time needed to find this data. In addition, users have been able to design new intra- and interdisciplinary futures works whit data found.

关键词： information System Interoperability Data Integration Metadata Management Open Science

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Decentralization of DAOs: A Fundamental Analysis 20th

Decentralization of DAOs: A Fundamental Analysis

引用

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

作者： Papangelou, Stamatis Christodoulou, Klitos Themistocleous, Marinos Univ Nicosia Inst Future IFF Sch Business Dept Digital Innovat Nicosia Cyprus

ISBN: (纸本)9783031564772;9783031564789

This paper addresses a significant gap in the current understanding of decentralization within Decentralized Autonomous Organizations (DAOs). While many theoretical discussions generalize governance models, there is a lack of in-depth analysis of the unique governance structures and their inherent decentralization mechanisms in individual DAOs. To bridge this gap, the paper presents two primary contributions. First, it offers a foundational analysis of potential points of failure in decentralization within DAOs. Second, the study introduces a "decentralization layer system", a new framework aimed at guiding future quantitative analyses of decentralization. This system is based on two pillars: a critical evaluation of governance structures and a stratification approach to identify distinct layers within these structures. The paper further demonstrates the application of this framework by analyzing the governance models of several DAOs, including Uniswap, Compound, and ApeCoin. Through these analyses, the paper provides insights into the strengths and vulnerabilities of eachDAO's decentralization mechanisms, offering a comprehensive perspective on DAO governance.

关键词： DeFi DAOs Governance Decentralization

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Discovering Process-Based Drivers for Case-Level Outcome Explanation 5th

Discovering Process-Based Drivers for Case-Level Outcome Exp...

引用

5th International Conference on Process Mining (ICPM)

作者： Li, Peng Zhang, Hantian Chu, Xu Seeliger, Alexander Yu, Cong Georgia Tech Atlanta GA USA Celonis New York NY 10007 USA

ISBN: (纸本)9783031561061;9783031561078

Process mining has shown great impact in improving business Key Performance Indicators (KPIs), which are typically measured as aggregations over case-level outcomes. A commonly encountered key question in achieving such impact is understanding the underlying reasons for why a certain outcome appears in some cases (e.g., why certain cases take long to finish). We use the term drivers to refer to explanations for case-level outcomes. We hypothesize that how process is run, in other words, process traces, directly influences case-level outcomes, and hence KPIs. In this paper, we propose a new method to automatically and efficiently discover process-based drivers that are effective, significant and interpretable. We formally define the problem of driver discovery as a constrained optimization problem. Given that the problem is NP-hard, we develop efficient greedy algorithms to solve the problem. We evaluate our method on real-world datasets to demonstrate the effectiveness and efficiency of our approach.

关键词： Process Mining Explanation Case outcome

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Voice Assistants - Research Landscape 20th

Voice Assistants - Research Landscape

引用

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

作者： Almirabi, Alaa Mehandjiev, Nikolay Sarantopoulos, Panagiotis Univ Manchester Alliance Manchester Business Sch Booth St West Manchester M13 0PB Lancs England

ISBN: (纸本)9783031564772;9783031564789

AI-powered Voice Assistants (VAs) emerge as attractive facilitators of the increasing interactions between people and machines in the Metaverse. VAs are still at early stages of adoption, so this systematic literature review charts the landscape of existing VA research with a focus on their use in different context. This helps us identify important aspects that require further examination. The findings indicate that while academic interest in this novel subject is increasing, the literature regarding the factors that drive continuous use does not take into account VAs' intelligence. Indeed, users perceive such devices as collaborative sentient actors rather than as tools. Taking these perceptions into consideration can help to increase the engagement of users with VAs.

关键词： AI Voice assistant (VA) Usage Interactions

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Generating Process Anomalies with Markov Chains: A Pattern-Driven Approach 5th

Generating Process Anomalies with Markov Chains: A Pattern-D...

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5th International Conference on Process Mining (ICPM)

作者： Veldman, Jochem Lu, Xixi Van der Waal, Wouter Dees, Marcus De Weerd, Inge Van Univ Utrecht Utrecht Netherlands UWV Amsterdam Netherlands

ISBN: (纸本)9783031561061;9783031561078

Generating anomalies for process executions helps to train anomaly detection methods and evaluate their performance. Anomalous behavior tends to be diverse and very infrequent. Generating process anomalies can help compare detection models and select the suited ones. However, little research has been focused on generating anomalous behavior in a systematic and also stochastic way. In this paper, we built on the idea of training a Markov chain using an event log to capture regular process behavior. We then use a set of predefined anomaly patterns to adapt the Markov chain to generate anomalous traces. To evaluate the quality of our generated anomalies, we use them in the downstream task training a detection model. For each pattern, we vary the quantity of injected anomalous traces and their deviation rate. Unsurprisingly, the results show that the models trained with the generated anomalies have a significant improvement in detecting these anomalies. The AUC score increased from 0.63 to reaching a maximum of 0.98 or higher for all three patterns. This confirms our expectation that generating anomalies can help train and evaluate detection models.

关键词： Stochastic systems

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Towards an Explorable Conceptual Map of Large Language Models 1

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

作者： Bertetto, Lorenzo Bettinelli, Francesca Buda, Alessio Da Mommio, Marco Di Bari, Simone Savelli, Claudio Baralis, Elena Bernasconi, Anna Cagliero, Luca Ceri, Stefano Pierri, Francesco Politecn Torino Turin Italy Politecn Milan Milan Italy

ISBN: (数字)9783031610004

ISBN: (纸本)9783031609992;9783031610004

Large Language Models (LLMs) have revolutionized the current landscape of Natural Language processing, enabling unprecedented advances in text generation, translation, summarization, and more. Currently, limited efforts have been devoted to providing a high-level and systematic description of their properties. Today's primary source of information is the Hugging Face (HF) catalog, a rich digital repository for researchers and developers. Although it hosts several models, datasets, and applications, its underlying data model supports limited exploration of linked information. In this work, we propose a conceptual map for describing the landscape of LLMs, organized by using the classical entity-relationship model. Our semantically rich data model allows end-users to answer insightful queries regarding, e.g., which metrics are most appropriate for assessing a specific LLM performance over a given downstream task. We first model the resources available in HF and then show how this map can be extended to support additional concepts and more insightful relationships. Our proposal is a first step towards developing a well-organized, high-level knowledge base supporting user-friendly interfaces for querying and discovering LLM properties.

关键词： Conceptual Modeling Knowledge Graph Large Language Models Knowledge Exploration Knowledge Management

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Understanding the Impact of Design Choices on the Performance of Predictive Process Monitoring 5th

Understanding the Impact of Design Choices on the Performanc...

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5th International Conference on Process Mining (ICPM)

作者： Kim, Sungkyu Comuzzi, Marco Di Francescomarino, Chiara Ulsan Natl Inst Sci & Technol Ulsan South Korea Univ Trento Trento Italy

ISBN: (纸本)9783031561061;9783031561078

Predictive process monitoring (PPM) aims at creating models that predict aspects of interest of process execution using historical data available in event logs, mostly using machine learning (ML) techniques. When developing a PPM model, one has several design choices, encompassing both ML-related concerns, such as which classification or regression model to choose, and PPM-specific concerns, such as how to encode the trace prefixes or whether to drop infrequent activities when training a model. While the literature has seen a few attempts to study how these choices impact the performance of a PPM model, no systematic studies on this matter exist. This paper moves towards closing this gap. We propose a framework to interpret the impact of design choices on the performance of a PPM model. The proposed framework uses as building blocks a search space exploration algorithm, which is able to generate different model configurations, and explainable AI techniques, e.g., SHAP, to analyze the impact of design choices on the model performance based on the generated configurations. We show an instantiation of the framework in the use case of outcome-oriented PPM, discussing also the experimental results obtained using publicly available event logs.

关键词： business Process Prediction Guidelines

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Exploiting Machine Learning Technique for Attack Detection in Intrusion Detection System (IDS) Based on Protocol 6th

Exploiting Machine Learning Technique for Attack Detection i...

引用

6th International Conference on information and Knowledge Systems (ICIKS)

作者： Aladesote, Olomi Isaiah Fakoya, Johnson Tunde Agbelusi, Olutola Fed Polytech Dept Comp Sci Ile Oluji Ondo Nigeria Fed Univ Technol Akure Dept Software Engn Akure Ondo Nigeria

ISBN: (纸本)9783031516634;9783031516641

An intrusion detection system (IDS) can be either software or hardware that computerizes the process of keeping track of and evaluating network or computer system activity for indications of security issues. IDS is a crucial component of the security infrastructure of many organizations due to an increase in the frequency and intensity of attackers over the past decades. The study proposes machine learning techniques for the classification and detection of normal and attack traffics using protocol types records of the NSL-KDD dataset. Three sets of datasets were extracted from NSL-KDD datasets based on ICMP, UDP, and TCP. The experiment was conducted on WEKA 3.8.5 using KNN, KStar, LWL, BayesNet, Naive Bayes, and PART algorithms. The results indicated that the PART algorithm has the highest performance rating while NaiveBayes has the lowest performance rating utilizing the Correlation-based feature selection (CFS) using the Ranking Filter approach. It is concluded that the PART algorithm performs well across the dataset while NaiveBayes does not perform well across the dataset.

关键词： Correlation-based feature selection Intrusion Detection System NSL-KDD dataset

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Upper Performance Limits and Distribution Invariance for Surrogate Weights in MCDA 24th

Upper Performance Limits and Distribution Invariance for Sur...

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24th International Conference on Group Decision and Negotiation (GDN)

作者： Lakmayer, Sebastian Danielson, Mats Ekenberg, Love Stockholm Univ Dept Comp & Syst Sci POB 7003 SE-16407 Kista Sweden Int Inst Appl Syst Anal Schlosspl 1 AT-2361 Laxenburg Austria

ISBN: (纸本)9783031593727;9783031593734

This paper proposes a simple approach to determining the upper performance limit (approximate the maximum hit ratio) for surrogate weight models in additive models of multi-criteria decision analysis. The approach is called the Approximate Maximum Hit Ratio (AMHR). As a partial result, simulations show that hit ratios resulting from so-called mean ordered weights as surrogate weight vectors resemble the AMHR reasonably well. Further, we scrutinise the case of different distributions for the sampling of alternative values, with the result that it seems to be invariant, i.e. the general behaviour of surrogate weight methods does not change significantly. We also study the use of different filters on the AMHR. Finally, the corresponding already well-performing methods (e.g., ROC, RS, and SR) show good overall results. Thus, we conclude that the existing methods are fairly effective and provide decision-makers (individual or in groups) with a valuable means of efficiently eliciting and dealing with imprecise criteria information.

关键词： Approximate maximum hit ratio Multi-criteria decision analysis Criteria weights Criteria ranking Distribution invariance Performance limits Surrogate weights

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The Role of Artificial Ethics Principles in Managing Knowledge and Enabling Data-Driven Decision Making in Supply Chain Management 20th

The Role of Artificial Ethics Principles in Managing Knowled...

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

作者： Alhaili, Saeeda Mir, Farzana British Univ Dubai Dubai U Arab Emirates

ISBN: (纸本)9783031564772;9783031564789

In today's data-driven business environment, the ethical management of knowledge and data utilization for decision-making in supply chain management has become increasingly vital. This study explores how artificial ethics principles can guide businesses in managing knowledge ethically and enable data-driven decision-making in supply chain management. The study specifically looks into two key areas: establishing moral standards for handling data and knowledge throughout the supply chain and incorporating artificial ethics principles into data analytics systems to support fairness and impartiality. The study follows a semi-systematic review approach. The findings show the importance of ethical considerations and their contributions to knowledge management and data-driven decision-making in supply chain management. By integrating artificial ethics principles, organizations can uphold ethical values such as accountability, fairness, and transparency in their decision-making procedures. Moreover, integrating these principles into data analytics systems ensures unbiased and equitable decision-making. This study emphasizes the value of integrating ethics into supply chain operations and provides advice for businesses looking to use data ethically and efficiently.

关键词： Knowledge Management Supply Chain Management Artificial Intelligence Machine Learning Artificial Ethics Principles

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