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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Virtual Avatar Movement Veracity and its Effect on Social Interaction in Virtual Reality

Virtual Avatar Movement Veracity and its Effect on Social In...

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Computing Conference

作者： Ram, Mira Loeb, Lorie Cohen, Michael Mahoney, James Dartmouth Coll Hanover NH 03755 USA Meta Real Labs Res Burlingame CA USA

ISBN: (纸本)9783031622830;9783031622816;9783031622809

The market for Virtual Reality (VR) products is growing rapidly, and we expect an important use case will be social interaction. This work contributes to the understanding of how social interaction and connection are impacted by how closely a user's real-life physical motion is tied to the motion of their virtual character, an "avatar." We observed social interaction between 16 pairs of participants (32 participants in total) controlling VR avatars over four different movement conditions in a within-subjects experimental setup: directly-captured (movement directly captured from VR headset and hand controllers), predictive (a mixture of directly captured movement and predicted synthetic movement), synthetic (movement entirely based on pre-generated animations), and a static control group. Two key metrics were analyzed to gauge social connection: expressivity and engagement, with a significant effect of avatarmovement condition on expressivity measures. The findings of this study suggest that users feel that they are best able to express themselves and that the expressions of others are best communicated when VR avatars feature a mixture of directly-captured and predictive synthetic movement. We also use this experiment to present a methodology that could be used for exploring other similar questions in VR.

关键词： Virtual reality Avatars Human computer interaction

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Proposal for a Model for Diabetes Detection Using Machine Learning Techniques 1

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13th Computer Science Online Conference

作者： Carrera, Benny Ccompi, Williams Evangelista, Gerson Ticona, Wilfredo Univ ESAN Lima Peru Univ Tecnol Peru Lima Peru

ISBN: (数字)9783031705182

ISBN: (纸本)9783031705175;9783031705182

Currently a very relevant problem is diabetes, this disease is the cause of thousands of deaths and tends to grow in the coming years. The objective of the present research is to detect diabetes using Machine Learning techniques. For this purpose, the Indian PIMA database (PID) was used, which was extracted from Kaggle. Preprocessing techniques such as null value treatment and feature normalization were applied. Finally, various learning models such as Multilayer Perceptron, Neural networks, DecisionTree, SupportVector Machines, K-Nearest Neighbors, Logistic Regression, Random Forest and Gradient Boosting algorithm were developed. From the best results obtained from the evaluation of the models, it was obtained that the best of them is the one generated by the Multilayer Perceptron Neural networks and Support Vector Machine whose metrics were superior in Accuracy with 84.62% and Recall with 76.92%;however, in Precision it was surpassed by the Random Forest algorithm with 81.81%. It follows that both the Multilayer Perceptron and Support Vector Machine models accurately predicted the onset of diabetes, thus establishing their effectiveness in this predictive task, offering invaluable assistance to the healthcare sector.

关键词： Machine Learning Algorithms Diabetes Prediction Hyperparameter Metrics

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FairGLVQ: Fairness in Partition-Based Classification 15th

FairGLVQ: Fairness in Partition-Based Classification

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15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM)

作者： Stoerck, Felix Hinder, Fabian Brinkrolf, Johannes Paassen, Benjamin Vaquet, Valerie Hammer, Barbara Bielefeld Univ Cognit Interact Technol CITEC Inspirat 1 D-33619 Bielefeld Germany

ISBN: (纸本)9783031671586;9783031671593

Fairness is an important objective throughout society. From the distribution of limited goods such as education, over hiring and payment, to taxes, legislation, and jurisprudence. Due to the increasing importance of machine learning approaches in all areas of daily life including those related to health, security, and equity, an increasing amount of research focuses on fair machine learning. In this work, we focus on the fairness of partition- and prototype-based models. The contribution of this work is twofold: 1) we develop a general framework for fair machine learning of partition-based models that does not depend on a specific fairness definition, and 2) we derive a fair version of learning vector quantization (LVQ) as a specific instantiation. We compare the resulting algorithm against other algorithms from the literature on theoretical and real-world data showing its practical relevance.

关键词： Fair Machine Learning Fairness Partition-based Models Prototype-based Models Fair LVQ

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Design and Development of a Differential Drive Platform for Dragster Competition 6th

Design and Development of a Differential Drive Platform for ...

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6th Iberian Robotics Conference (Robot)

作者： Grilo, Vinicius Ferreira, Edilson Barbosa, Ana Chellal, Arezki Abderrahim Lima, Jose Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300253 Braganca Portugal Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Regioes M Braganca Portugal Univ Tras Os Montes & Alto Douro P-5000801 Vila Real Portugal

ISBN: (纸本)9783031591662;9783031591679

Robotics competitions have been increasing in the last years since they bring several impacts on students education, such as technical skill development, teamwork, resilience and decision making withing the STEM skills. The article highlights the significance of robotics competitions as platforms for fostering innovation and driving advancements in the field of robotics. This article primarily focuses on the development of a robot in the Dragster category for the 2023 Portuguese Robotics Open. It outlines the strategies devised to tackle the competition's challenges and discusses the obstacles encountered along with the corresponding solutions employed. The article delves into the specific details of the challenges faced and the iterative processes undertaken to enhance the robot's performance and functionalities. By sharing the insights gained from the project, future proposals for iterations of the robot will be presented, aiming to further augment its features and overall performance while sharing knowledge with other teams and community.

关键词： Robot development Dragster category Competition challenges

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Extending the Learning Factory Through Virtual Reality 14th

Extending the Learning Factory Through Virtual Reality

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14th International Conference on Learning Factories (CLF)

作者： Bjornsten, Thomas Bogevald Wade, Tine Juhl Nellemann, Camilla Christiansen, Lasse Via Univ Coll Horsens Denmark Mfg Acad Denmark Copenhagen Denmark Aalborg Univ Aalborg Denmark Univ Coll Northern Denmark Aalborg Denmark

ISBN: (纸本)9783031654107;9783031654114

The Learning Factory approach has offered authentic solutions to mimic industrial products and processes in higher education, industrial upskilling and primary education. However, the potential can be increased by adopting a virtual layer through the so-called virtually extended learning factories in simulation, augmented reality, and virtual reality. These dynamics have generated promising results, but the relation between the setup's visuals, sound, haptics, and adaptability remains to be investigated. This paper aims to demonstrate the correlation between attention paid to a given object in a virtual reality simulation to critically evaluate selected functionalities that we consider crucial for a potential, later application of the knowledge. We assess the attention through eye-tracking within the virtual reality environment, which extends a physical learning factory into the virtual space. The Learning Factory-context pertains to enhancing operational efficiency, where participants are tasked with executing a sequence of procedures on a simulated Festo plant. The objective is to augment the Overall Equipment Effectiveness (OEE). The paper demonstrates how the virtual reality environment provides a flexible user interface and scenario that can potentially enhance the understanding of industrial procedures within a learning factory. Hence, leveraging virtual reality is a way to impart key operations management terminologies like overall equipment effectiveness, single-minute exchange of dies, and takt/cycle time while emphasizing their practical applications. However, this comes with a technological entry barrier, which reduces the potential for learners unfamiliar with virtual reality technologies.

关键词： Virtual Reality Eye-tracking Multimodality Adaptability Technological entry barrier

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Boosting Octree Color Quantization Descriptor for Content Based Image Retrieval: A Case Study 4th

Boosting Octree Color Quantization Descriptor for Content Ba...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Hassan, Zekkouri Yousra, Zarouit Brahim, Aksasse Mohammed, Ouanan Mohamed, Ouhda Moulay Ismail Univ Fac Sci & Tech Errachidia Dept Comp Sci BP 509 Boutalamine Errachidia 52000 Morocco Moulay Ismail Univ Fac Sci Meknes Dept Comp Sci Meknes Morocco Sultan Moulay Slimane Univ Higher Sch Technol Dept Comp & Math TIAD Lab Khenifra Morocco

ISBN: (纸本)9783031686528;9783031686535

Color quantization (CQ) plays a crucial role in image processing with reducing the number of colors in an image or of bins in a color histogram. This operation is particularly important in Content-Based Image Retrieval (CBIR) when using color histogram-based descriptors. Among various image processing techniques, the Octree tree data structure stands out as a powerful tool for performing CQ. By manipulating two key parameters, namely the number of colors K and the depth of the tree D, diverse outcomes can be achieved with the Octree. In this study, multiple experiments were conducted to determine the optimal values of K and D that yield the highest precision for the Color Octree Quantization Descriptor (COQD). COQD is a descriptor that utilizes the Octree data structure to quantize images and subsequently extracts features from them based on a color string coding (CSC). The experiments undertaken in this case study demonstrate enhancements in precision.

关键词： Color Histogram Color Descriptors Octree Quantization Content Based Image Retrieval Image Retrieval

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Deep Feature Discriminability as a Diagnostic Measure of Overfitting in CNN Models 1

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Computing Conference

作者： Sikha, O. K. Benitez, Raul Univ Politecn Catalunya UPC BarcelonaTECH Automat Control Dept Barcelona 08034 Spain Inst Recerca St Joan de Deu IRSJD Barcelona 08950 Spain

ISBN: (数字)9783031622816

ISBN: (纸本)9783031622830;9783031622816;9783031622809

In computer vision, a deep understanding of overfitting phenomena in Convolutional Neural networks(CNNs) plays a pivotal role in developing novel, advanced Deep Learning architectures. In this study, we present a novel methodology that identifies and analyzes model overfitting by leveraging unsupervised clustering of the features extracted by CNNs. Our research demonstrates that overfitted models exhibit inadequate class discriminability within the feature space. To further delve into the distribution of classes in the deep feature space, our study combines conventional cluster separability metrics with model selection methods, resulting in a comprehensive characterization of the training process and the resulting trained architecture. We show that poor training procedures, such as overfitting or insufficient training data, directly compromise the discriminative capability of deep features in terms of two novel evaluation metrics: cluster-discriminability and cluster-class similarity scores. We also investigate the impact of transfer learning and dropout regularization on overfitting. Experimental results on benchmark datasets for classification tasks validate that transfer learning is less susceptible to overfitting, and incorporating dropout regularization effectively mitigates overfitting tendencies and enhances the generalization capabilities of CNNs. Furthermore, we unveil that the clusterdiscriminability score can serve as a metric to distinguish between normal and adversarial inputs, making it a valuable tool for detecting adversarial attacks in CNNs.

关键词： Deep convolutional neural networks Overfitting in CNNs Interpreting overfitting Explainable AI

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How is the Use of ICT Shaping Our Intended Effort for Learning? 10th

How is the Use of ICT Shaping Our Intended Effort for Learni...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Zrinka, Fiser Univ Slavonski Brod Dept Social Sci & Humanities EDUDpt Slavonski Brod 35000 Croatia

ISBN: (纸本)9783031662706;9783031662713

During COVID-19 pandemic, students of all educational levels were forced to transition from face-to-face to online learning setting which necessitated the use of ICT and different learning platforms such as Zoom, Microsoft Teams or Google Meet. During the 2022/2023 academic year, 279 non-English major students at the University of Slavonski Brod in Croatia participated in a study to determine the effect of online learning experience on the process of learning English as a foreign language (EFL). Kendall's tau-b, Mann-Whitney U, and t-tests revealed a strong positive correlation between intended effort and the experience of online learning during tertiary education. The instrument used in the research has not yet been applied in the context of postpandemic Croatian universities, so the findings represent a frontier in exploring the influence of using ICT in formal learning of EFL.

关键词： ICT pandemic intended effort online learning students

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Analyzing the Industry 5. Adoption Barriers in the Pharmaceutical Sector Using an Integrated Fuzzy MCDM Methodology: A Case Study Application for Turkey 1

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Maden, Ayca Beykent Univ Istanbul Turkiye

ISBN: (数字)9783031671951

ISBN: (纸本)9783031671944;9783031671951

Industry 5.0, emerging as a response to the mechanistic approach of Industry 4.0, seeks to integrate human-centric perspectives with advanced technologies to address global challenges. This transformative concept is reshaping sectors like pharmaceuticals, introducing novel manufacturing paradigms to meet evolving consumer demands. However, the pharmaceutical industry faces adoption barriers hindering its transition to Industry 5.0. This study analyses these barriers within the Turkish pharmaceutical sector using an integrated MCDM methodology. As a result of evaluations obtained from decision-makers, the use of fuzzy numbers was deemed necessary in this study. A key focus of this study involves applying the Fuzzy DEMATEL method to ascertain the significance of critical Industry 5.0 barriers. Additionally, the research expands its analysis by employing the EDAS method to prioritize the functions within a specific company. The EDAS method yielded the following ranking for the functions of the company: manufacturing, regulatory affairs, research and development, quality control and assurance, and sales and marketing. This research contributes to advancing Industry 5.0 implementation strategies, fostering innovation, and enhancing competitiveness within the pharmaceutical sector.

关键词： Industry 5.0 barriers DEMATEL EDAS pharmaceutical sector Fuzzy theory

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Enriching Software Engineering Gamification Environments with Social Information 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Pedreiral, Oscar Garcia, Felix Piattini, Mario Fernandez-Aleman, Jose Luis Kharbouch, Manal Univ A Coruna Fac Informat Elvilla S-N La Coruna 15071 Spain Univ Castilla La Mancha Paseo Univ 4 Ciudad Real 13071 Spain Univ Murcia Fac Informat Campus Univ 32 Murcia 30100 Spain

ISBN: (数字)9783031456459

ISBN: (纸本)9783031456442;9783031456459

Motivation: There has been a recent focus on the potential benefits of using gamification techniques to increase motivation and improve performance in Software Engineering environments. Problem: While many gamification proposals in Software Engineering utilize techniques such as points, levels, and rankings, there is some skepticism about the effectiveness of these basic techniques and some authors explored other complementary alternatives. Approach: This paper presents ongoing research on using social information analysis techniques as a complementary alternative to enhance gamified Software Engineering environments. Result: the inclusion of social information into gamified environments was proposed. To accomplish this, the relevant information to be delivered to users and the data sources for this information were identified. A simple infrastructure for integrating and processing social information was also proposed. Impact: If successful, this approach could potentially have a significant impact on the motivation and performance of people in Software Engineering environments.

关键词： Software Engineering Gamification Social Information

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