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

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Reviewing Crypto-Agility and Quantum Resistance in the Light of Agile Practices

Reviewing Crypto-Agility and Quantum Resistance in the Light...

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24th International Conference on Agile Software Development (XP)

作者： Marchesi, Lodovica Marchesi, Michele Tonelli, Roberto Univ Cagliari Dept Math & Comp Sci Cagliari Italy

ISBN: (纸本)9783031485497;9783031485503

The term crypto-agility means the ability to quickly and securely change cryptographic algorithms and related data, in the case of their compromise. In this context, the advent of quantum computing constitutes a new paradigm, which poses existential threats to current cryptographic algorithms. Even if these attacks are not an imminent danger, we must be prepared to change the cryptographic algorithms at risk with new, quantum resistant ones. This is by no means an easy task, because cryptographic algorithms are used everywhere and are often also implemented on the hardware. In this paper, we analyze the similarities and the differences between traditional agility and crypto-agility, and investigate the prospects of using agile and lean practices in the context of crypto-agility to introduce quantum resistant algorithms. In particular, for the main agile and lean practices we discuss if and how they can be useful for obtaining crypto-agility. We also investigate how the features key to crypto-agility can be helped by the agile and lean approach.

关键词： Agile methods Cryptographic agility Encryption algorithms Quantum resistance

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Process-Specific Extensions for Enhanced Recommender Systems in business Process Management 1

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

作者： Petter, Sebastian Jablonski, Stefan Univ Bayreuth Bayreuth Germany

ISBN: (数字)9783031610035

ISBN: (纸本)9783031610028;9783031610035

In business Process Management (BPM) the integration of advanced recommender systems emerges as a critical strategy to enhance process efficiency and user satisfaction. Despite the dissemination of these systems, there remains a distinct lack of incorporating execution relevant context data, particularly those generated during process execution and prevailing environmental conditions. This paper addresses this gap by proposing process-specific extensions for augmenting an existing recommender system framework. Our approach not only enhances the adaptability and accuracy of recommendations but also sustains the applicability of existing algorithms, ensuring seamless integration into available recommender frameworks. The potential of this refined approach is demonstrated by evaluating a process scenario based on synthetic data.

关键词： business process management Recommender systems User-centered process improvement

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Framework for Understanding Consumer Perceptions and Attitudes to Support Decisions on Cultured Meat: A Theoretical Approach and Future Directions 10th

Framework for Understanding Consumer Perceptions and Attitud...

引用

10th International Conference on Decision Support Systems Technologies (ICDSST)

作者： Pivoraite, Guoste Liu, Shaofeng Roh, Saeyeon Zhao, Guoqing Univ Plymouth Plymouth Business Sch Plymouth 4 8AA England Swansea Univ Sch Management Swansea SA1 8EN W Glam Wales

ISBN: (纸本)9783031593758;9783031593765

This paper investigated consumer perceptions and attitudes for decision making in Cultured Meat (CM), driven by the growing interest in innovative food products. The motivation stemmed from the anticipated challenges in consumer acceptance of CM, a novel alternative to traditional meat production. The research objective included to identify key factors influencing consumer behaviour in the context of the novel food product. The Systematic Literature Review-methodically explored and synthesised existing research, giving insights to the factors affecting consumer perceptions and attitudes towards decisions on CM. Then, a tailored conceptual framework, the CulturedMeat Attitude and Perception Assessment (CAPA), has been developed to address the identified gaps and limitations in understanding consumer perceptions and attitudes. The results highlighted the complex and multidimensional nature of consumer attitudes, emphasising the role of knowledge (awareness, comprehension, familiarity), perception (disgust, neophobia, curiosity, fear, trust), and external factors (ethical issues, social factors, product attributes, information influence, perceived exclusivity, regulatory considerations) that could be used by decision makers such as food innovators and marketers. The CAPA framework integrated these factors to offer a holistic perspective on consumer behaviour, overcoming the limitations of existing work and offering insights to the decision makers in the industry.

关键词： Consumer Behaviour Decision Factors Food Innovation Cultured Meat

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Supporting Interpretability in Predictive Process Monitoring Using Process Maps 25th

Supporting Interpretability in Predictive Process Monitoring...

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25th International Conference on Enterprise information Systems (ICEIS)

作者： Maita, Ana Rocio Cardenas Fantinato, Marcelo Peres, Sarajane Marques Maggi, Fabrizio Maria Univ Sao Paulo Sch Arts Sci & Humanities Sao Paulo Brazil Free Univ Bozen Bolzano Fac Comp Sci Bolzano Italy

ISBN: (纸本)9783031647475;9783031647482

Most predictive process monitoring approaches rely on machine learning techniques. These approaches predict, e.g., the outcome of a process case. As widely known, many machine learning techniques do not inherently provide insights in a useful format for business process experts to interpret the provided predictions and understand the logic used to derive such predictions. Recently, we proposed VisInter4PPM, a business-oriented approach to visually support interpretability in predictive process monitoring. In this paper, we apply VisInter4PPM to a loan request business process, whose behavior is represented in a real-world event log of a financial institution. This is a multiclass prediction problem where requests can be approved, declined, or cancelled. VisInter4PPM relies on the results of the SP-LIME interpreter to generate explanations about the influence of each business process activity on the case outcome. Thus, the SP-LIME results are visually projected onto a BPMN process model. The resulting process map shows which activities contribute to the predicted outcome and to what extent.

关键词： Process mining Explainable machine learning XAI Interpretable machine learning Predictive process monitoring

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Imposing Rules in Process Discovery: An Inductive Mining Approach 18th

Imposing Rules in Process Discovery: An Inductive Mining App...

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

作者： Norouzifar, Ali Dees, Marcus van der Aalst, Wil RWTH Univ Aachen Germany UWV Employee Insurance Agcy Amsterdam Netherlands

ISBN: (纸本)9783031594649;9783031594656

Process discovery aims to discover descriptive process models from event logs. These discovered process models depict the actual execution of a process and serve as a foundational element for conformance checking, performance analyses, and many other applications. While most of the current process discovery algorithms primarily rely on a single event log for model discovery, additional sources of information, such as process documentation and domain experts' knowledge, remain untapped. This valuable information is often overlooked in traditional process discovery approaches. In this paper, we propose a discovery technique incorporating such knowledge in a novel inductive mining approach. This method takes a set of user-defined or discovered rules as input and utilizes them to discover enhanced process models. Our proposed framework has been implemented and tested using several publicly available real-life event logs. Furthermore, to showcase the framework's effectiveness in a practical setting, we conducted a case study in collaboration with UWV, the Dutch employee insurance agency.

关键词： process mining process discovery domain knowledge

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Exploring Gamification in Process Mining Education: Towards a Playful and Engaging Approach 5th

Exploring Gamification in Process Mining Education: Towards ...

引用

5th International Conference on Process Mining (ICPM)

作者： Delcoucq, Landelin Univ Mons UMONS Mons Belgium

ISBN: (纸本)9783031561061;9783031561078

This article explores how gamification, i.e. the integration of game elements, of the teaching of Process Mining enhances its effectiveness. By combining traditional academic teaching with game elements, gamification stimulates student engagement, facilitates understanding of complex concepts and encourages active learning. This article defines what gamification is (what), what it can bring to the teaching of Process Mining (why), how it can be implemented (how) as well as its limitations and how its development can be envisaged based on strong theoretical concepts and data gathered from the audience concerned.

关键词： Teaching Gamification Process Mining

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Use of Artificial Intelligence in Occupational Health and Safety in Construction Industry: A Proposed Framework for Saudi Arabia 11th

Use of Artificial Intelligence in Occupational Health and Sa...

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

作者： Khahro, Shabir Hussain Khahro, Qasim Hussain Prince Sultan Univ Coll Engn Dept Engn Management Riyadh Saudi Arabia Univ Tun Hussein Onn Malaysia Fac Civil Engn & Built Environm Jamilus Res Ctr Batu Pahat 86400 Johor Malaysia

ISBN: (纸本)9783031581120;9783031581137

Occupational accidents have always been very important for all industries due to its significant impacts on projects and society. Whereas the case in construction industry is also similar due to higher number of occupational accidents recorded in various countries. In current dynamic technological era, conventional Health and Safety (H&S) practices are not sufficient thus new approaches are important to explore. It is observed from the detailed literature review that there are various existing tools designed by various researcher in different scenario but there is a lack to device an integrated approach. Therefore, this paper proposes an integrated approach to monitor the H&S at site. The proposed framework will help the decision makers to manage and monitor the health and safety practices at site in a real time environment.

关键词： Construction Safety Artificial Intelligence Big Data Worker Health Future Technologies

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Implementing Object-Centric Event Data Models in Event Knowledge Graphs 5th

Implementing Object-Centric Event Data Models in Event Knowl...

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5th International Conference on Process Mining (ICPM)

作者： Swevels, Ava Fahland, Dirk Montali, Marco Eindhoven Univ Technol Eindhoven Netherlands Free Univ Bozen Bolzano Bolzano Italy

ISBN: (纸本)9783031561061;9783031561078

Recent advances in object-centric process mining necessitated the standardization of object-centric event data (OCED). An IEEE taskforce has developed a "meta-model" for OCED, but there is no existing reference implementation or automated techniques to transform legacy data into OCED. This task requires domain-specific knowledge about the semantics of the legacy data in order to make explicit how events act on various inter-related data objects and their attributes. We propose a semantic header that defines how extracted legacy data maps to OCED concepts and the domain-specific reference ontology using PG-schema. We automatically translate the header into database queries to construct an event knowledge graph that is compliant with OCED and the domain ontology using a declarative extract-load-transform approach. The approach has been implemented and demonstrated on 7 real-life datasets, making it one of the first attempts to make OCED operational.

关键词： OCED Semantic Header Event Knowledge Graph PG-Schema ELT Framework

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Selection of Rapid Classifier Development Methodology Used to Implement a Screening Study Based on Children's Behavior During School Lessons 24th

Selection of Rapid Classifier Development Methodology Used t...

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

作者： Dziczkowski, Grzegorz Jach, Tomasz Probierz, Barbara Stefanski, Piotr Kozak, Jan Univ Econ Katowice Dept Machine Learning 1 Maja 50 PL-40287 Katowice Poland Inst Innovat Technol EMAG Lukasiewicz Res Network Leopolda 31 PL-40189 Katowice Poland

ISBN: (纸本)9783031593727;9783031593734

The purpose of the article is to prepare a methodology for an advanced system that implements screening among children of early school age. The screening will be implemented in classrooms using cameras. Cameras in bi-weekly windows will study children's behavior and the system will report alerts when abnormal behavior is detected. The alerts are intended to recommend in-depth examinations with a specialist. In this article, the authors present a preliminary study to assess the feasibility of rapidly creating classifiers that detect specific behavioral elements (e.g., open mouth, putting fingers in mouth, asymmetrical closing of eyes, etc.). The article aims to define a methodology for detecting anomalies in children's behavior, which in the next stages of the project will be used to detect undesirable behaviors such as lack of concentration, hyperactivity, epilepsy, undesirable behavior to noise and stress. The aim of the presented research is to create a methodology based on proprietary neural network-based classifiers in later studies implementing screening tests. The presented article presents research comparing the performance of two different neural network architectures: an advanced ResNet-based model and a simpler custom convolutional neural network (CNN). The research presented here demonstrates that both advanced and simple models have their place in the rapid development of microclassifiers and allow acceptance of the chosen methodology for further work on student screening.

关键词： Computer Vision Behavior analysis machine learning knowledge discovery in databases

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Identifying Relevant Data in RDF Sources 18th

Identifying Relevant Data in RDF Sources

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

作者： Chevallier, Zoe Kedad, Zoubida Finance, Beatrice Chaillan, Frederic Univ Versailles Paris Saclay DAVID Lab Versailles France Grand Paris Sud Evry France

ISBN: (纸本)9783031594670;9783031594687

The increasing number of RDF data sources published on the web represents an unprecedented amount of information. However, querying these sources to extract the relevant information for a specific need represented by a target schema is a complex task as the alignment between the target and the source schemas might not be provided or incomplete. This paper presents an approach which aims at automatically populating the classes of a target schema. Our approach relies on a semi-supervised learning algorithm that iteratively identifies instance patterns in the data source that represent candidate instances for the target schema. We present some preliminary experiments showing the effectiveness of our approach.

关键词： RDF data sources Target Schema Instantiation Web Data extraction Semi-supervised learning

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