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

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K-means for Small Earthquakes. Alternative Disaggregation Analyses by Considering Wave Components and Soil Types 2nd

K-means for Small Earthquakes. Alternative Disaggregation An...

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2nd International Conference on information Technologies and Their Applications

作者： Zacchei, Enrico Brasil, Reyolando Itecons Coimbra Portugal Univ Coimbra CERIS Coimbra Portugal Univ Sao Paulo Polytech Sch Sao Paulo 380 Prof Luciano Gualberto Sao Paulo SP Brazil Fed Univ ABC UFABC Ctr Engn Modeling & Appl Social Sci 3 Rua Arcturus Sao Bernardo Do Campo SP Brazil

ISBN: (纸本)9783031734199;9783031734205

Prediction of ground motion by earthquakes is still the main issue for seismologists, geo-technicians, engineers, and researchers. In this paper k-means algorithm has been used to disaggregate seismic parameters to evaluate their inter-correlations. A goal is to quantify the weights and effects of each parameter with respect other ones. From database, about 4900.0 data, divided in 22 categories, have been collected. The main divisions regard the wave components in horizontal and vertical axis, and the soil characteristics. The studied seismic zone is the "Norpirenaica oriental", placed at Pyrenees area between Spain and France, classified as a very high seismic hazard. Numerical and analytical analyses have been carried out to implement the algorithm. These analyses could quantify the role of the sand horizontal stratigraphy, the non-linear response, and the elasticity of the soil. Results are plotted in stochastic distributions and elastic spectra accelerations. Rigorously, results are valid only for the studied seismogenic zone under predefined constrictions and range.

关键词： AI Machine Learning k-means Pyrenees Area Seismic Analyses

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Arm in Motion: How Motion Modality and Erratic Behavior of a Robotic Arm Shape User Perception 1

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Liberman-Pincu, E. Oron-Gilad, T. Ben Gurion Univ Negev IL-84105 Beer Sheva Israel

ISBN: (数字)9783031826337

ISBN: (纸本)9783031826320;9783031826337

This study examines the impact of motion design on user perceptions and interactions with robotic arms, which are expanding from industrial applications to supporting daily human tasks. The research investigates how different motion components influence user perceptions and preferences. Employing an online questionnaire, we analyze participants' responses to videos of robotic arms performing laundry sorting tasks, revealing significant relationships between motion modalities, erratic behavior, and characteristic attribution. The findings contribute to the design of robotic arm movements to effectively communicate and engage with users, enhancing human-robot interaction in both industrial and social contexts.

关键词： Motion design Robotic arm Human-Robot interaction Human-Robot relationship

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Comparative Study Using Ensemble Methods and Sampling Techniques for Imbalanced Diabetes Data 4th

Comparative Study Using Ensemble Methods and Sampling Techni...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Khemisa, Hana Dendani, Nadjette Khedimi, Yasmine Amara, Nour Djihane Azizi, Nabiha Badji Mokhtar Univ Dept Comp Sci Annaba Algeria

ISBN: (纸本)9783031821523;9783031821530

In medical decision making, the efficacy of classification, a vital component of decision support systems, can be significantly compromised by imbalanced class distributions within training data. This discrepancy is especially critical due to the heightened consequences of misclassification, particularly when a minority class is mistakenly classified as a majority class. Diabetes, posing a substantial public health challenge, underscores the urgency of addressing classification issues. The World Health Organization reports that 30% to 40% of diabetes cases remain undiagnosed, contributing to a 3% increase in diabetes-related mortality rates from 2000 to 2019. In 2019 alone, approximately 2 million deaths were attributed to diabetes and related kidney diseases. Consequently, effective intervention strategies are imperative. Managing imbalanced datasets in medical contexts presents significant obstacles to accurate classification, with profound implications for patient outcomes. To address this challenge, this research proposes leveraging machine learning and artificial intelligence techniques. The research comprises two main components: applying boosting and unbalanced bagging to a dataset containing diabetes-related information. Initial findings indicate that boosting yields promising results in classification accuracy. Building upon this success, we compare boosting with three additional technique Synthetic Minority Over-sampling Technique (SMOTE), Edited Nearest Neighbors (ENN), and Tomek links each sequentially applied to the dataset. Through rigorous experimentation and analysis, we aim to identify the most effective strategy for handling imbalanced datasets in diabetes classification. By enhancing our understanding of classification methods and their impact on imbalanced datasets, this research aims to contribute to improved decision-making processes in healthcare settings, ultimately leading to better outcomes for patients affected by diabetes.

关键词： Machine Learning Classification Imbalanced Data Diabetes Sampling Ensemble methods

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User Identification Based on a Photoplethysmography Sensor for Biometrics in Smart Environments 8th

User Identification Based on a Photoplethysmography Sensor f...

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Rocha, Ana Patricia Almeida, Nuno Silva, Ana Luisa Correia, Pedro Leitao, Catia Senra, Hugo Costa, Florinda Teixeira, Antonio Univ Aveiro Inst Elect & Informat Engn Aveiro Dept Elect Telecommun & Informat Aveiro Portugal Univ Aveiro i3N Inst Nanostruct Nanomodelling & Nanofabr Phys Dept Aveiro Portugal

ISBN: (纸本)9783031838446;9783031838453

The spaces we live in are becoming increasingly smarter due to the integration of more and more sensors that acquire a large amount of information not only on the environment, but also on the people living in it. In this context, human identification is important not only to know who the collected data corresponds to, but also to guarantee that only authorised people can access the data and that information presentation is adapted to the user. In the scope of ongoing research projects aiming at health and well-being monitoring in smart homes, the main objective of this contribution is to explore the feasibility of user identification through non-invasive sensors, namely photoplethysmography (PPG) sensors. We propose a solution based on transfer learning and features corresponding to images (scalograms) extracted from the PPG signals to obtain a model for user identification. The results of the model's evaluation, using a dataset obtained from ten subjects (three acquisitions each), show that an acquisition-independent solution can be challenging, even when considering a small interval between acquisitions, achieving a mean accuracy of 73% and mean F1 score of 72% (two acquisitions for training and a single window for classification). Nevertheless, these results show that there is potential in using a low-cost PPG sensor for biometrics in smart environments, especially if identity is decided based on several consecutive windows. This solution has different possible applications related to human monitoring, access control (e.g., rooms, devices, appliances), and adaptive interaction (e.g., adaptation of the output to the current user).

关键词： User identification Biometrics Smart environments Non-intrusive sensing Photoplethysmography Transfer learning

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Designing an Accessible Music Device for the Elderly: Enhancing Usability, Entertainment, and Well-Being 1

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

作者： Desai, Akshay Indian Inst Technol IDC Sch Design Mumbai Maharashtra India

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

With the growth in technology, digital devices have largely replaced analogue devices. While this change is welcomed by many, the elderly users often struggle to adapt due to several reasons. This paper explores the possibility of designing a digital music device for elderly users in the Indian context. It examines the limitations, issues, and cultural aspects that affect their interaction with technology, and how these challenges can be addressed to create a more accessible and user-friendly device.

关键词： Elderly Accessible Design Music player NFC

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Intelligent Monitoring System for Construction Sites 1

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Landolfi, Enrico Troiano, Alfredo Cajora, Alessia D'Onghia, Simone Mario Barrasso, Tommaso Colace, Francesco Netcom Engn SpA Naples Italy Univ Salerno Fisciano Italy

ISBN: (数字)9783031832079

ISBN: (纸本)9783031832062;9783031832079

The construction industry continues to face significant challenges in ensuring the safety of workers on construction sites. Recognized as one of the most dangerous work environments, construction sites still report high accident rates despite significant safety efforts. This paper presents a new personnel monitoring system on construction sites with the primary goal of improving safety levels and significantly decreasing the incidence of work accidents. The proposed system is an IoT hardware/software platform based on a geo-fencing system and a Deep Learning model that performs inferences on images acquired from the construction site. Specifically, key functionalities include accurate counting of workers in monitored areas and verification of the presence of personal protective equipment by each worker, while also detecting unauthorized personnel. Rigorous real-world testing has demonstrated the feasibility of the system, especially in terms of real-time operational effectiveness. The results obtained were encouraging to plan further interesting developments on the topic of construction site safety.

关键词： IoT Deep Learning Decision Making

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OpenStreetMap-Based Mapping Tool to Support TREEADS Holistic Fire Management Ecosystem for Prevention, Detection and Restoration of Environmental Disasters 5th

OpenStreetMap-Based Mapping Tool to Support TREEADS Holistic...

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5th 5th Olympus - International Conference on Supply Chain

作者： Zapounidis, Konstantinos Koidis, Christos Sakkos, Nikolaos Bantsos, Athanasios Aptalidis, Irodotos Aidonis, Dimitrios Achillas, Charisios Engineers Business SA 79 Dekatis Evdomis Noemvriou St Thessaloniki 55534 Greece Int Hellen Univ Kanelopoulou 2 Katerini 60100 Greece

ISBN: (纸本)9783031693502;9783031693519

Considering the socio-ecological transition of Europe 2030, and towards a more resilient and informed community, focusing on the forests that are near wildfire risk, TREEADS project aims to build upon state-of-the-art high TRL (Technology Readiness Level) products and unite them in a Holistic Fire Management Platform that optimize and reuse per phase the available socio-technological resources in all three main phases of wildfires. For the prevention and preparedness, TREEADS project proposes the use of a real-time risk evaluation tool that can receive multiple classification inputs and work with a new proposed neural network-powered Risk Factor Indicator. TREEADS uses a variety of technological solutions such as the Copernicus infrastructure, and a swarm of small drones customized for accurate forest supervision. In the area of detection, TREEADS proposes a variety of toolsets that will accommodate most needs, stemming from virtual reality for the training, wearables for the protective equipment of the emergency responders to UAV (drones), UAG and airships for improving capacity in temporal and spatial analysis as well as to increase the inspected area coverage. The TREEADS technologies and services will be validated in 8 complex pilot implementations (6 EU countries, 1 Associate Country and Taiwan). EFB will provide pilots with the OpenStreetMap (OSM)-based Mapping Tool, which is a cross-platform compatible app that visualizes static and dynamic information fetched from relevant REST APIs. It is designed to display points of interest (POIs), events, and directions or suggestions based on simulated scenarios in natural environments such as forests and gorges. The tool combines the OSM database with the OpenLayers and/or Leaflet libraries and the OpenRouteService and/or OSRM and/or Mapbox map services. By combining these powerful tools, OSM-based Mapping Tool offers a comprehensive solution for visualizing geographic data in an intuitive and user-friendly way. All t

关键词： OpenStreetMap Mapping Tool Fire Management

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Benchmarking Political Bias Classification with In-Context Learning: Insights from GPT-3.5, GPT-4o, LLaMA-3, and Gemma-2 5th

Benchmarking Political Bias Classification with In-Context L...

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5th Southern African Conference for Artificial Intelligence Research

作者： Kotze, Eduan Senekal, Burgert A. Univ Free State Dept Comp Sci & Informat Bloemfontein South Africa

ISBN: (纸本)9783031782541;9783031782558

ChatGPT, which provides access to the large language models GPT-3.5 and GPT-4, received wide attention worldwide after its release in 2022, with many noting the great potential this conversational agent holds, the many dangers and limitations of this technology. This study investigates the capabilities of large language models, specifically GPT-3.5, GPT-4o, GPT-4o mini, LLaMA-3 and Gemma-2 for classifying political bias (left, centre, and right) using a specialised dataset. The models were evaluated for their performance in text classification, focusing on accuracy, precision, recall, and F1 scores. The results indicate that GPT-4o outperforms GPT-4o mini, GPT-3.5 Turbo, LLaMA-3, and Gemma-2 for in-context learning. GPT-4o also surpassed the TF-IDF word bi-gram SVM baseline for this text classification task;however, none of the other models achieved better results, as they frequently misclassified biased sentences as unbiased. Despite its strengths, GPT-4o still exhibits some limitations. Overall, the findings suggest that GPT-4o is a promising tool for in-context learning in detecting political bias, as it generally assigns the correct classifications.

关键词： GPT-3.5 Turbo GPT-4o LLaMA-3 Gemma-2 political bias large language models in-context learning text classification

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Utilizing LDPE as a Second Life of Waste Plastic Through Pellet Extruder 3D Printing 5th

Utilizing LDPE as a Second Life of Waste Plastic Through Pel...

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5th 5th Olympus - International Conference on Supply Chain

作者： Pemasi, Sotirios Kladovasilakis, Nikolaos Pechlivani, Eleftheria Maria Natsios, Ioannis Melidis, Lazaros Chortis, Alexandros Konstantinidis, Avraam A. Katakalos, Konstantinos Tzetzis, Dimitrios Tzovaras, Dimitrios Anastasiou, Eleftherios Ctr Res & Technol Hellas Informat Technol Inst 6th Km Charilaou Thermi Rd Thessaloniki 57001 Greece I4bydesign Competence Ctr PC 6th Klm Charilaou Thermis Thessaloniki 57001 Greece Aristotle Univ Thessaloniki Fac Engn Sch Civil Engn Lab Expt Strength Mat & Struct Thessaloniki 54124 Greece Aristotle Univ Thessaloniki Fac Engn Sch Civil Engn Lab Engn Mech Thessaloniki 54124 Greece Int Hellen Univ Sch Sci & Technol Digital Mfg & Mat Characterizat Lab Thessaloniki Greece Aristotle Univ Thessaloniki Dept Civil Engineer Lab Bldg Mat Thessaloniki 54124 Greece

ISBN: (纸本)9783031693502;9783031693519

Progressing plastics circularity and its associated benefits can provide advantages to both practitioners and the broader environment. As technology continues to evolve and various industries increasingly embrace the principles of the circular economy, additive manufacturing cannot remain detached. The widespread utilization of additive manufacturing can be a valuable asset for reprocessing recycled plastics, for reuse as secondary life products. This study investigates the reuse of Low-density PolyEthylene (LDPE) in additive manufacturing via a robust, customized, low-cost pellet extruder 3D printing system. The system underwent evaluation through mechanical tests of 3D printing specimens and Finite Element Analyses (FEA). Four mixtures were created using different fractions of neat and recycled LDPE and tensile specimens were fabricated and tested under quasi-static tests, according to ASTM D638. The corresponding FE materials models were developed to create a numerical tool for the evaluation of LDPE 3D printed components. The tensile strength tests showed that specimens developed with 100% recycled LDPE performed 70.06% better in elastic modulus, 18.18% better in ultimate tensile strength (UTS), and 17.74% better in yield strength compared to specimens consisting of 60% recycled and 40% virgin LDPE, which exhibited the best mechanical properties among the tested mixtures. However, they exhibited a 14.34% reduction in elongation at break. The outcomes of this study established a sustainable and fully functional proof-of-concept of the developed 3D printing system to address the reduction, reuse and recycling of plastic waste under the Sustainable Plastic Strategy.

关键词： LDPE Circular Economy Additive Manufacturing 3D printing Mechanical Testing Finite Element Analysis plastic wastes second-life products

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Client Security Alone Fails in Federated Learning: 2D and 3D Attack Insights 1st

Client Security Alone Fails in Federated Learning: 2D and 3D...

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1st MICCAI Meets Africa Workshop

作者： Parampottupadam, Santhosh Floca, Ralf Bounias, Dimitrios Hamm, Benjamin Roy, Saikat Sav, Sinem Zenk, Maximilian Maier-Hein, Klaus German Canc Res Ctr Div Med Image Comp Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Heidelberg Germany Bilkent Univ Dept Comp Engn Ankara Turkiye Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp D-69120 Heidelberg Germany

ISBN: (纸本)9783031791024;9783031791031

Federated learning (FL) plays a vital role in boosting both accuracy and privacy in the collaborative medical imaging field. The importance of privacy increases with the diverse security standards across nations and corporations, particularly in healthcare and global FL initiatives. Current research on privacy attacks in federated medical imaging focuses on sophisticated gradient inversion attacks that can reconstruct images from FL communications. These methods demonstrate potential worst-case scenarios, highlighting the need for effective security measures and the adoption of comprehensive zero-trust security frameworks. Our paper introduces a novel method for performing precise reconstruction attacks on the private data of participating clients in FL settings using a malicious server. We conducted experiments on brain tumor MRI and chest CT data sets, implementing existing 2D and novel 3D reconstruction techniques. Our results reveal significant privacy breaches: 35.19% of data reconstructed with 6 clients, 37.21% with 12 clients in 2D, and 62.92% in 3D with 12 clients. This underscores the urgent need for enhanced privacy protections in FL systems. To address these issues, we suggest effective measures to counteract such vulnerabilities by securing gradient, analytic, and linear layers. Our contributions aim to strengthen the security framework of FL in medical imaging, promoting the safe advancement of collaborative healthcare research. The source code is available at: https://***/MIC- DKFZ/2D3DPrivacy-Attacks-In-federated-Medical-Imaging.

关键词： Federated Learning Privacy Attacks Medical Imaging Reconstruction Attacks

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