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检索条件"丛书名=Communications in Computer and Information Science"

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HOTS: A Containers Resource Allocation Hybrid Method Using Machine Learning and Optimization 7th

HOTS: A Containers Resource Allocation Hybrid Method Using M...

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7th International Conference on Optimization and Learning

作者： Leclercq, E. Rivalan, Jonathan Smile Ile De France France

ISBN: (纸本)9783031779404;9783031779411

One of the main challenges in cloud computing is resource management, the ability to schedule workloads and services over the infrastructure in the most automated way. By optimizing cloud assignment and resource usage, energy can be saved, production incident can be anticipated and services QoS improved. With the recent years emergence of light virtualisation, known as containerization, the resource allocation problem was brought back, notably to support containers elasticity, hence the dynamic allocation of ressource at runtime at a single service scale. In this paper we show that using an hybrid loop system, which combines unsupervised learning and optimization techniques, our algorithm provides and iteratively improves scheduling solutions to containers resource assignment, enabling capacity planning over dynamic resource loads. Within our benchmarks, these solutions outperform state of the art algorithms, by an average of 6.3%, while providing more expressivity and control over input parameters. We describe also the implementation of this method, through an open source Python library called HOTS, which allows hybrid optimization for time series based use cases.

关键词： Clustering Continuous Optimization Containerized Applications Resource Allocation

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Optimisation-Based Classification Tree: A Game Theoretic Approach to Group Fairness 7th

Optimisation-Based Classification Tree: A Game Theoretic App...

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7th International Conference on Optimization and Learning

作者： Liapis, Georgios I. Papageorgiou, Lazaros G. UCL Univ Coll London Dept Chem Engn Sargent Ctr Proc Syst Engn Torrington Pl London WC1E 7JE England

ISBN: (纸本)9783031779404;9783031779411

The growing use of machine learning algorithms in decisions that significantly affect people necessitate interpretable and fair approaches. Mathematical programming based machine learning models have attracted attention because of the flexibility they provide to integrate features like interpretability and fairness, combined with high accuracy. This work introduces a mathematical programming based classification tree that uses a game theoretic approach to address group fairness. The proposed mathematical formulation is a Mixed Integer Linear Programming model using a piecewise linearisation strategy based on special-ordered sets. The overall misclassification rate is the fairness metric examined and the Nash bargaining scheme is followed to balance the trade off between the misclassification error of the groups. The efficiency of the methodology is evaluated via three binary and multi-class literature datasets, which provide evidence for the fairness and accuracy of the predictions made by the model.

关键词： Machine learning Fairness Mathematical programming Data classification Game theory

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Optimizing Classification of Congestive Heart Failure Using Feature Weight Importance Correlation 1st

Optimizing Classification of Congestive Heart Failure Using ...

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1st MICCAI Meets Africa Workshop

作者： Oronti, Iyabosola B. Haleem, Muhammad S. Pecchia, Leandro Popham, Thomas Univ Warwick Coventry CV4 7AL England Queen Mary Univ London 327 Mile End Rd London E1 4NS England Univ Campus Bio Medico Roma Via Alvaro Portillo 21 I-00128 Rome Italy

ISBN: (纸本)9783031791024;9783031791031

In this work, a novel method for selecting the optimal set of input features for classifying the presence of congestive heart failure (CHF) using a supervised machine learning approach is presented. A random forest classifier (RFC) was utilized to carry out the binary classification task and two different models were explored. We employed the embedded RFC feature importance attribute for the first model, and a multi-classifier technique which integrates the feature weight importance correlation (F-WIC) method was adopted for the second model. Our results show that the second model using the F-WIC method offers superior performance (100% accuracy) and provides a generalized approach to feature engineering for machine learning models irrespective of the algorithm used. This work offers a novel method for selecting the optimal set of input features for classifying the presence of congestive heart failure (CHF) using a machine learning approach.

关键词： machine learning feature engineering congestive heart failure

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Quantification of Cherry Production Impact with the Use of Life Cycle Assessment Methodology 5th

Quantification of Cherry Production Impact with the Use of L...

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5th 5th Olympus - International Conference on Supply Chain

作者： Spanos, Konstantinos Zapounidis, Konstantinos Galiagousi, Iliana Aidonis, Dimitrios Int Hellen Univ Sch Econ & Business Adm Dept Supply Chain Management Katerini 60100 Greece Pieriki Anaptixiaki SA OLA Pieriki Dev Agcy Katerini 60100 Greece

ISBN: (纸本)9783031693502;9783031693519

This study investigates the environmental impact of cherry production of the "Agios Loukas" Agricultural Cooperative of Rachis Pieria. For the quantification of the impact, the Life Cycle Assessment (LCA) methodology is used. The key objective of the work is to comprehensively document all stages of cherry production and associated processes in order to assess different environmental impacts' indicators. In brief, LCA assesses how the entire life cycle of a product influences the environment. The impact quantification employs the LCArt Simulations Engine, a web application developed under the KYKLOS 4.0 project, funded by the EU Horizon 2020 Programme. The methodology follows ReCipe 2016 v1.03 midpoint (H). Data for the year 2022 were sourced, with actual field data provided by cooperative executives and management. Data collection involved interviews with both the cooperative's scientific staff and its members.

关键词： Agriculture LCA Cherries Environmental impacts ReCipe 2016

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ARoma: Augmented Reality Olfactory Menu Application 1

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15th Indian Conference on Human-computer Interaction Design and Research

作者： Balachandran, Aarav Gupta, Kritika Vohra, Prajna Srivastava, Anmol Indraprastha Inst Informat Technol Delhi IIIT Del Delhi India

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

This study aims to explore the integration of Augmented Reality (AR) and olfactory technology to enhance dining experience in restaurants. We present ARoma, an innovative AR olfactory menu application for Indian cuisine which provides users with 3D visualisation of dishes, detailed ingredient and nutritional information, and historical context, as well as an olfaction device to deliver the aroma of the dishes. Our research compares the traditional menu experience with the AR menu and ARoma, aiming to understand how these technologies affect customers' perceptions of food quality, dining enjoyment, and immersion. Our user study involved a sample size of 30 participants, divided into two groups. Group A compared traditional menu experiences with AR menus, while Group B experienced traditional menus followed by ARoma. Using this control group study and mixed-method approach, including quantitative surveys and qualitative interviews, we found that AR menus significantly enhance the dining experience by providing detailed and engaging information. Our findings suggest that AR and olfactory technology can significantly improve customer satisfaction and engagement in the food industry.

关键词： Augmented Reality Olfaction AR-Menu Dining Experience Olfactory devices Scent Dispersion

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Medical Procedures Based on Bayesian Network and Possibilistic Model Checking 42nd

Medical Procedures Based on Bayesian Network and Possibilist...

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42nd National Conference on Theoretical computer science

作者： Wen, Ying He, Qing Li, Yongming Shaanxi Normal Univ Sch Math & Stat Xian Shaanxi Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9789819614899;9789819614905

In this paper, we present a method that integrates Bayesian network and generalized possibilistic model checking to improve the accuracy of initial diagnostics and quantify uncertainties in the rehabilitation process. Bayesian network provides probabilistic decision support using data-driven statistical information from various sources. Computational tree logic, combined with generalized possibility measures, effectively manages the uncertainties associated with patient rehabilitation.

关键词： Model checking Bayesian network Generalized possibilistic computation tree logic (GPoCTL) Generalized possibilistic Kripke structure (GPKS)

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Predictors of Workplace Satisfaction: Working Onsite VersusWorking from Home 4th

Predictors of Workplace Satisfaction: Working Onsite VersusW...

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4th International Conference on Society 5.0

作者： Wittmann, Xinhua Klyushina, Daria Univ Appl Sci & Arts Northwestern Switzerland Sch Business Windisch Switzerland Univ Zurich Fac Econ Business Adm & Informat Zurich Switzerland

ISBN: (纸本)9783031714115;9783031714122

The Covid-19 pandemic has not only taught us about viral infections, but also provided employers with unique opportunities to experiment with different work modes. What leads employees to be more satisfied: working onsite or working from home? The aim of this paper is to elucidate how the work environment impacts workplace satisfaction. In the framework of Herzberg's motivation theory, we define the physical, digital, and social environment as the most important hygiene factors. In our study, we investigate the relationship between these hygiene factors and workplace satisfaction. Based on the survey data from office workers in Switzerland, we identify the predictors of workplace satisfaction for working onsite and working from home respectively. Our statistical analysis shows that the three hygiene factors have a significant positive impact on workplace satisfaction both for working onsite and working from home. Specifically, the workplace design in the context of the physical environment stands out to have the strongest effect on the workplace satisfaction for onsite work. In contrast, for working from home, the software availability in the context of the digital environment exerts the greatest influence on workplace satisfaction. Thus, workplace satisfaction can be maximized by targeted optimization of specific aspects of the work environment depending on the work mode.

关键词： Workplace satisfaction Working onsite Working from home Workplace environment Hygiene factors Physical environment Social environment Digital environment

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A Recommender System for Mining Personalized User Preferences 5th

A Recommender System for Mining Personalized User Preference...

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5th China Intelligent Robotics Academic Conference

作者： Li, Huiying Chen, Fei Wang, Houda Dalian Maritime Univ Dalian 116026 Liaoning Peoples R China

ISBN: (纸本)9789819616138;9789819616145

This study proposed a personalized recommender system based on LLM and explored how to better capture and utilize user preferences to increase recommendation accuracy and explainability. Traditional recommender methods, such as collaborative filtering, content-based, and hybrid approaches, often overlook the relationships between user preference attributes. This study leverages LLM, considers the relationships between user preference attributes, and combines it with a self-attention mechanism to achieve fine-grained scoring of user preferences. The system delved into user preferences through dialog, adjusting the recommendation content in each turn of the dialog. The experimental results demonstrated significant improvements in both the recommendation accuracy and the quality of the generated dialog content. Specifically, on the ReDial dataset, Recall@10 improved by 2.9%, and the informativeness increased by 3%. The study also emphasized the importance of attribute sorting order, further enhancing the system's explainability.

关键词： Personalized recommender LLM User preference Self-attention mechanism

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Impact of Job Scheduling Policy Changes on User Behaviour and System Response: The Case of the Santos Dumont Supercomputer in Brazil 11th

Impact of Job Scheduling Policy Changes on User Behaviour an...

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11th Latin American Conference on High Performance Computing

作者： dos Santos, Joao Pedro M. N. Gomes, Antonio Tadeu A. Lab Nacl Computacao Cientif LNCC Petropolis RJ Brazil

ISBN: (纸本)9783031800832;9783031800849

The highly heterogeneous workloads generated by current scientific and technological research complicate job scheduling in supercomputers, making commercial scheduling tools challenging to be configured effectively. Recent studies have analysed these workloads, but the impact of job scheduling policy changes on both job input and system output variables remains largely unexplored. This paper aims at a methodology for characterising both users' and system's behaviour in response to policy changes. As a case study, we use the Santos Dumont supercomputer at the National Laboratory for Scientific Computing (LNCC) in Brazil;we examine its job accounting records over two years, with a policy change in between them targeting a reduction in waiting times. Key findings of our study include differential impacts of the policy change on jobs with varying waiting times and the utility of grouping users to interpret eventual behavioral changes due to the policy change.

关键词： Job scheduling User behaviour Exploratory analysis

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Disaggregation Analyses of the External/Internal Parameters for Chloride Diffusion in Concrete Structures: Artificial Intelligence by k-means Algorithm 2nd

Disaggregation Analyses of the External/Internal Parameters ...

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2nd International Conference on information Technologies and Their Applications

作者： Zacchei, Enrico Bastidas-Arteaga, Emilio Hamani, Ameur Itecons Coimbra Portugal Univ Coimbra CERIS Coimbra Portugal Rochelle Univ Lab Engn Sci Environm LaSIE CNRS 7356 UMR UMR Ave Michel Crepeau F-17042 La Rochelle France

ISBN: (纸本)9783031734199;9783031734205

In this paper, the parameters involved in the chloride ions diffusion for concrete structures have been analysed. Usually, several factors for diffusivity and surface chloride concentration are introduced to consider all internal and external actions that contribute to the diffusion. However, the inter-correlations between them are neglected. This paper treats these inter-correlations. From experimental laboratory tests about 850 parameters and values have been filtered and collected. These values have been divided in 11 categories and combined with each other. More of 120 numerical analyses by using k-means algorithm have been carried out. This algorithm is considered an artificial intelligence (AI) to support the human limitations in managing and analysing an enormous quantity of parameter. Results provide disaggregated values where each parameter is correlated with other one. This allows to understand their weight and effects on chloride ions diffusion. This paper provides not only a new approach but also practical values for more accurate analyses.

关键词： Chloride diffusion AI k-means disaggregation analyses

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