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检索条件"丛书名=IFIP Advances in Information and Communication Technology 461"

共 10561 条记录，以下是221-230 订阅

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Integrating Uncertainty into a Supply Chain Network for Adaptive S&OP Process 25th

Integrating Uncertainty into a Supply Chain Network for Adap...

引用

25th ifip WG 5.5 Working Conference on Virtual Enterprises (PRO-VE)

作者： Vidal, Jean Baptiste Oger, Raphael Lauras, Matthieu Lamothe, Jacques Univ Toulouse Ctr Genie Ind IMT Mines Albi Albi France

ISBN: (纸本)9783031717413;9783031717390;9783031717383

Instability is the new normal for supply chain networks. In this context, the Adaptive Sales and Operations Planning (AS&OP) process proposed by the Demand-Driven Adaptive-Enterprise model aims to improve the way these supply chain networks manage volatile, uncertain, complex and ambiguous environments. However, the process, as originally defined, relies mainly on a deterministic maximum likelihood approach, thus limiting its scope. The present research work examines existing strategies for integrating uncertainty into the strategic planning and ultimately proposes an original Decision Support System that integrates uncertainty via interval data and scenario-based planning. An illustrative case study validates this proposal and discusses its limitations.

关键词： Adaptive S&OP Demand-Driven Uncertainty Supply chain Network

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A Machine Learning Approach for Points of Interest Extraction and Event Classification 20th

A Machine Learning Approach for Points of Interest Extractio...

引用

20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Dias, Pedro Ferreira, Flora Guimaraes, Pedro M. F. Wojtak, Weronika Erlhagen, Wolfram Monteiro, Sergio Sousa, Emanuel Bicho, Estela Univ Minho Dept Ind Elect Braga Portugal Univ Minho Ctr ALGORITMI Braga Portugal Univ Minho Res Ctr Math Braga Portugal CCG ZGDV Inst Ctr Comp Graph Guimaraes Portugal

ISBN: (纸本)9783031632259;9783031632235;9783031632228

This paper presents a novel approach that utilizes machine learning techniques, specifically clustering algorithms and artificial neural networks, to improve the prediction and understanding of human routines in urban mobility contexts. Our method focuses on the identification and categorization of Points of Interest (POIs) from travel data, facilitating the accurate prediction of user routines for intelligent transportation systems. By integrating a clustering phase that groups individual stop points into POIs, followed by a correction mechanism through user interaction, we address the limitations of existing methods in adapting to dynamic mobility patterns and the contextual ambiguity of GPS coordinates. Subsequently, a classification phase employs a feed-forward neural network to assign incoming travel events to the identified POIs. This dual-phase approach not only improves the precision of routine predictions but also enhances the adaptability of the system to changes in mobility behavior over time. The incorporation of a cognitive module, based on Dynamic Neural Fields (DNF), further allows for personalized predictions regarding the timing, duration, and nature of trips. Validated with datasets from the Portuguese city of Braga, our results demonstrate the effectiveness of this methodology in providing actionable insights for the development of cognitive solutions for the project ***'s innovative vehicle "BEN". By emphasizing user involvement and algorithmic transparency, our work contributes to the advancement of smart transportation technologies in urban environments.

关键词： Clustering algorithms Artificial Neural Networks Points of Interest (POIs)

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Risk and Control Assurance Framework for Emergency Management Projects 8th

Risk and Control Assurance Framework for Emergency Managemen...

引用

8th International Conference on information technology in Disaster Risk Reduction (ITDRR)

作者： Radianti, Jaziar Khazanchi, Deepak Univ Agder Dept Informat Syst Kristiansand Norway Univ Nebraska Omaha Informat Syst & Quantitat Anal Omaha NE USA

ISBN: (纸本)9783031640391;9783031640377;9783031640360

It has been suggested that the phases of Emergency Management align well with standard project management stages. In this sense, the EMprojects have a lifecycle that starts with initiation and ends with the closing phase. We use a project management assurance (PMA) lens to argue that like any other project, there are risks at each phase of an emergency management project and these need to be understood and addressed during the management process. Thus, in this paper, we propose a Risk and Control Assurance framework applied for project activities during the stages of the emergency management process. We propose two Emergency Project Management (EPM) models from a PMA perspective: First, ProjectModel 1 illustrates EMprojects that encompass all organizational routines within EM, including planning, preparation, response, recovery, and the mitigation of disaster impacts. Second, Project Model 2 encompasses software or tool support development intended to facilitate the tasks of emergency management practitioners across various stages of the lifecycle of emergency management.

关键词： Project Management Assurance Risks and Control Emergency Management IT Project Management

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An AI-Based Approach to Identify Financial Risks in Transportation Infrastructure Construction Projects 1

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20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Ivanova, Marina Grims, Michael Karas, Daniel Hoefinger, Gerhard Bornschlegl, Marco Xaver Hemmje, Matthias L. STRABAG Innovat & Digitalisat A-1220 Vienna Austria Univ Hagen Fac Math & Comp Sci D-58097 Hagen Germany

ISBN: (数字)9783031632150

ISBN: (纸本)9783031632174;9783031632150;9783031632143

"In a perfect world, free from budgets, every piece of data that is collectable would be collected, and every byte would be analyzed [...]". Big data analysis frameworks have already found their way into mainstream application and have seen wide-spread deployment in scientific communities as well as in organizations across different industry fields. Moreover, also AI-support is a necessary requirement for modern (big data) analysis applications nowadays. An exemplar industrial application domain highlighting the necessity of AI-supported data exploration in a real-world big data analysis application scenario is the risk analysis (economic risks) of building and construction projects. Currently, economical risk analysis is largely based on so-called expert knowledge, an experience- and intuition-based analysis of the risk. Even this experience-based (manual) process can be formalized in different ways, "construction projects are characterized by carrying a high level of uncertainty and complexity". With a strong focus on the project execution phase, this paper outlines how ML algorithms can be applied to automatically predict the financial outcome based on financial controlling data and thus to potentially assist in mitigation of financial losses.

关键词： Data Science Artificial Intelligence Machine Learning AI2VIS4BigData DARIA

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Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration 1

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39th International Conference on ICT Systems Security and Privacy Protection (SEC)

作者： Kalutharage, Chathuranga Sampath Liu, Xiaodong Chrysoulas, Christos Bamgboye, Oluwaseun Edinburgh Napier Univ Edinburgh Midlothian Scotland

ISBN: (数字)9783031651755

ISBN: (纸本)9783031651779;9783031651755;9783031651748

The perpetual evolution of cyberattacks, especially in the realm of Internet of Things (IoT) networks, necessitates advanced, adaptive, and intelligent defence mechanisms. The integration of expert knowledge can drastically enhance the efficacy of IoT network attack detection systems by enabling them to leverage domain-specific insights. This paper introduces a novel approach by applying Neurosymbolic Learning within the Explainable Artificial Intelligence (XAI) framework to enhance the detection of IoT network attacks while ensuring interpretability and transparency in decision-making. Neurosymbolic Learning synergizes symbolic AI, which excels in handling structured knowledge and providing explainability, with neural networks, known for their prowess in learning from data. Our proposed model utilizes expert knowledge in the form of rules and heuristics, integrating them into a learning mechanism to enhance its predictive capabilities and facilitate the incorporation of domain-specific insights into the learning process. The XAI framework is deployed to ensure that the predictive model is not a "black box", providing clear, understandable explanations for its predictions, thereby augmenting trust and facilitating further enhancement by domain experts. Through rigorous evaluation against benchmark IoT network attack datasets, our model demonstrates superior detection performance compared to prevailing models, along with enhanced explainability and the successful incorporation of expert knowledge into the adaptive learning process. The proposed approach not only fortifies the security mechanisms against network attacks in IoT environments but also ensures that the knowledge discovery and decision-making processes are transparent, interpretable, and verifiable by human experts.

关键词： Neurosymbolic learning Attack detection Explainable artificial intelligence Expert knowledge

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AI-Driven Sentiment Trend Analysis: Enhancing Topic Modeling Interpretation with ChatGPT 1

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20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Alharbi, Abdulrahman Hai, Ameen Abdel Aljurbua, Rafaa Obradovic, Zoran Temple Univ Ctr Data Analyt & Biomed Informat Philadelphia PA 19122 USA Jazan Univ Dept Comp Sci Jazan Saudi Arabia Qassim Univ Dept Comp Sci Buraydah Saudi Arabia

ISBN: (数字)9783031632150

ISBN: (纸本)9783031632174;9783031632150;9783031632143

Understanding the sentiment trends of large and unstructured text corpora is essential for various applications. Despite extensive application of sentiment analysis and topic modeling, extracting meaningful insights from the vast amount of textual data generated on social media platforms presents unique challenges due to the short and noisy nature of the text. In this study, we propose a methodology for analyzing sentiment trends in social media, including data collection, data preprocessing, sentiment analysis, social network graph construction, and topic modeling interpretation using ChatGPT. By integrating ChatGPT with topic modeling techniques such as LDA and BERTopic, we aim to enhance the interpretability of sentiment-related topics and gain deeper insights into sentiment trends in social media conversations. Through a case study focusing on parental hesitancy toward child vaccination, we illustrate the applicability and utility of our proposed methodology in real-world social media analysis scenarios, demonstrating its effectiveness in topic modeling interpretation and enhancing understanding of social media discourse. The integration of ChatGPT and BERTopic yielded improved topic interpretation for the short text of large corpus based on the coherence score of the original posts and generated description of the topic, ultimately reducing the cost and time required for topic interpretation by humans.

关键词： Sentiment analysis Social network analysis Topic modeling and Interpretation BERTopic ChatGPT

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Impact of Collaborative Robots on Human Trust, Anxiety, and Workload: Experiment Findings 43rd

Impact of Collaborative Robots on Human Trust, Anxiety, and ...

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43rd ifip WG 5.7 International Conference on advances in Production Management Systems (APMS)

作者： Montini, Elias Ploner, Giovanni Matteri, Davide Cutrona, Vincenzo Rocco, Paolo Bettoni, Andrea Pedrazzoli, Paolo Univ Appl Sci & Arts Southern Switzerland Dept Innovat Technol Lugano Switzerland Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9783031658969;9783031658945;9783031658938

This work proposes an experiment setup and its protocols to investigate the impact of cobot's size, speed and collaboration modes on different human factors including trust, propensity to trust, anxiety, and mental workload. The setup and the protocols supported the execution of different experiments where the 29 participants were asked to complete the Tower of Hanoi in collaboration with a cobot. The setup and the protocols provide a ready-to-use solution to expand experiments for further studies. Moreover, statistical analysis of the results shows higher cobot speeds increased trust propensity despite not significantly affecting overall trust or anxiety. Collaboration modes significantly influenced perceived workload and task performance, with the "Collaboration with Trigger" mode resulting in lower mental workload but longer task completion times. No significant differences were found in human factors concerning cobot size, indicating that variations in size do not significantly impact trust, propensity to trust, anxiety, or workload. Additionally, the collaboration mode with cobots notably affects workload perception and task performance, with specific modes reducing perceived effort but not necessarily improving task efficiency.

关键词： Cobots Human Robot Collaboration Collaborative robots Human factors Ergonomics

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A Proposal for Automatic Demand Forecast Model Selection 25th

A Proposal for Automatic Demand Forecast Model Selection

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25th ifip WG 5.5 Working Conference on Virtual Enterprises (PRO-VE)

作者： Garred, Wassim Oger, Raphael Barthe-Delanoe, Anne-Marie Lauras, Matthieu Univ Toulouse Ctr Genie Ind IMT Mines Albi Albi France

ISBN: (纸本)9783031717451;9783031717437;9783031717420

Demand forecasting is critical within collaborative networks, enabling suppliers, manufacturers, and retailers to synchronize their operations and achieve enhanced supply chain efficiency. Despite a wealth of research on time series forecast model selection and the availability of numerous forecast models, selecting the most appropriate model for a specific time series remains a challenging task. In this study, an automatic demand forecast model selection procedure is proposed that includes a wide range of statistical and machine learning forecast models. The optimization of the hyperparameters is performed on all the models. The study is validated on M3 monthly data, outperforming all submitted methods and demonstrating significant improvements in terms of accuracy. The approach was also applied to a collaborative network company.

关键词： Automatic model selection Time series Demand forecast Hyperparameter optimization

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Xproofs: New Aggregatable and Maintainable Matrix Commitment with Optimal Proof Size 39th

Xproofs: New Aggregatable and Maintainable Matrix Commitment...

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39th International Conference on ICT Systems Security and Privacy Protection (SEC)

作者： Yong, Xinwei Wu, Jiaojiao Wang, Jianfeng Xidian Univ Sch Cyber Engn Xian Peoples R China

ISBN: (纸本)9783031651779;9783031651755;9783031651748

Vector Commitment (VC) enables one to commit to a vector, and then the element at a specific position can be opened, with proof of consistency to the initial commitment. VC is a powerful primitive with various applications, including stateless cryptocurrencies. Recently, matrix commitment Matproofs (Liu and Zhang CCS 2022), as an extension of VC, has been proposed to reduce the communication and computation complexity of VC-based cryptocurrencies. However, Matproofs requires linear-sized public parameters, and the aggregated proof size may also increase linearly with the number of individual proofs aggregated. Additionally, the proof updating process involves a third party, known as Proof-Serving Nodes (PSNs), which leads to extra storage and communication overhead. In this paper, we first propose a multidimensional variant of matrix commitment and construct a new matrix commitment scheme for two-dimensional matrix, called 2D-Xproofs, which achieves optimal aggregated proof size without using PSNs. Furthermore, we present a highly maintainable three-dimensional scheme, 3D-Xproofs, which updates all proofs within sublinear time in the size of the committed matrix without PSNs' assistance. More generally, we could further increase the matrix dimensionality to achieve more efficient proof updates. Finally, we demonstrate the security of our schemes, showing that both schemes are position binding. We also implement both schemes, and the results indicate that our schemes enjoy constant-sized aggregated proofs and sublinear-sized public parameters, and the proof update time in 3D-Xproofs is 2.5x faster than Matproofs.

关键词： Vector commitment Matrix commitment Stateless cryptocurrency

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Integrated Dynamic Flexible Job Shop and AIV Scheduling: Deep Reinforcement Learning Approach Considering AIV Charging and Capacity Constraints 43rd

Integrated Dynamic Flexible Job Shop and AIV Scheduling: Dee...

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43rd ifip WG 5.7 International Conference on advances in Production Management Systems (APMS)

作者： Hosseini, Arman Feizabadi, Mohammad Yahouni, Zakaria Univ Virginia Syst Engn Charlottesville VA 22903 USA Univ Grenoble Alpes CNRS Grenoble INP G SCOP F-38000 Grenoble France

ISBN: (纸本)9783031716478;9783031716454;9783031716447

Scheduling Automated Intelligent Vehicles (AIV) in Dynamic Flexible Job Shops is a challenging problem due to its high level of stochasticity and dynamic nature. Various heuristic and exact methods have proven effective when the complexity of the problem is relatively low. Moreover, in the past decade, machine learning algorithms, particularly reinforcement learning, have been applied to sophisticated scheduling tasks and demonstrated their efficiency in solving such problems. In this study, a deep reinforcement learning approach is proposed to address the integrated Dynamic Flexible Job Shop and AIV scheduling. Aimed at optimizing two objectives: total lateness of jobs and total energy consumption of AIVs. The study takes into account the limitations of AIV transporters, such as charging consumption and loading capacity. To validate the proposed method, a case study of a flexible job shop is designed, and our approach is compared with a combination of existing heuristics.

关键词： AIV scheduling Flexible Job shop Manufacturing optimization Reinforcement learning Deep Q-network

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