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30th ieee Conference Virtual Reality and 3D User Interfaces (ieee VR)

作者： Marques, Bernardo Costa, Liliana Reisinho, Pedro Silva, Catia Silva, Samuel Zagalo, Nelson Univ Aveiro DeCA DigiMedia Aveiro Portugal Univ Aveiro IEETA DETI LASI Aveiro Portugal

ISBN: (纸本)9798350348392

The rapid changes experienced in the industrial development processes to ensure interconnectivity and customization have led to an increasing need to adopt collaborative design practices and consider end-users' context at earliest stages. Such contextual information generates large amounts of data that are usually oversimplistic, overlooking the spatial dimension in its representation and visualization. Usually, this is analyzed in post-processing using digital tools, which lack proper methods to analyze the information collected. This position paper proposes a vision for using data collection and Mixed Reality (MR). thus supporting industrial product co-design. This combination can be highly flexible in providing a perspective that overlays user feedback in a digital form on top of a physical artifact. As such, it may he possible to conduct co-design tasks in a more informed manner, having a higher level of awareness of the product characteristics. These and other arguments are presented and future directions are proposed.

关键词： Human-centered computing Human computer interaction (HCI) Interaction Paradigms Mixed/augmented reality Human-centered computing Human computer interaction (HCI) HCI Design and Evaluation Methods User Studies

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Deep Federated Learning for Autonomous Driving

Deep Federated Learning for Autonomous Driving

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33rd ieee Intelligent Vehicles symposium (ieee IV)

作者： Anh Nguyen Tuong Do Minh Tran Nguyen, Binh X. Chien Duong Tu Phan Tjiputra, Erman Tran, Quang D. Univ Liverpool Dept Comp Sci Liverpool Merseyside England AIOZ Singapore Singapore

ISBN: (纸本)9781665488211

Autonomous driving is an active research topic in both academia and industry. However, most of the existing solutions focus on improving the accuracy by training learnable models with centralized large-scale data. Therefore, these methods do not take into account the user's privacy. In this paper, we present a new approach to learn autonomous driving policy while respecting privacy concerns. We propose a peer-to-peer Deep Federated Learning (DFL) approach to train deep architectures in a fully decentralized manner and remove the need for central orchestration. We design a new Federated Autonomous Driving network (FADNet) that can improve the model stability, ensure convergence, and handle imbalanced data distribution problems while is being trained with federated learning methods. Intensively experimental results on three datasets show that our approach with FADNet and DFL achieves superior accuracy compared with other recent methods. Furthermore, our approach can maintain privacy by not collecting user data to a central server. Our source code can be found at: https://***/aioz-ai/FADNet

关键词： Training Privacy data privacy Deep architecture Collaborative work data models Stability analysis

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Federated Cox Proportional Hazards Model with multicentric privacy-preserving LASSO feature selection for survival analysis from the perspective of personalized medicine 35

Federated Cox Proportional Hazards Model with multicentric p...

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35th ieee International symposium on Computer-Based Medical Systems (CBMS)

作者： Masciocchi, Carlotta Gottardelli, Benedetta Savino, Mariachiara Boldrini, Luca Martino, Antonella Mazzarella, Ciro Massaccesi, Mariangela Valentini, Vincenzo Damiani, Andrea Fdn Policlin Univ Agostino Gemelli IRCCS Gemelli Generator Real World Data Rome Italy Fdn Policlin Univ Agostino Gemelli IRCCS Dip Diagnost Immagini Radioterapia Oncol & Ematol Rome Italy

ISBN: (数字)9781665467704

ISBN: (纸本)9781665467704

The Cox Proportional Hazards regression is among the most widely used models in clinical and epidemiological research for investigating the association between time-to-event outcomes and multiple predictors, that, in the modern perspective of personalized medicine, tend to belong to ever wider spheres relating to the patient and his medical condition. When the goal is to include a large number of variables in a prediction model, feature selection techniques are often required to ensure a certain level of interpretability of the results and federated learning is necessary to recruit in the study the sufficient number of patients for reliable model outcomes, overcoming the main problems of data privacy and ownership. In this regard, we here propose an adaptation for federated learning of the optimization algorithm of the Cox Proportional Hazards regression model with LASSO regularization as feature selector and we demonstrate the efficacy of our algorithm on real and simulated data sets in a simulated distributed environment with no patient-level data sharing by comparing its model parameter estimation performances with its centralised version.

关键词： Federated learning Privacy-preserving data mining Survival analysis Feature selection LASSO regularization Personalized medicine

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Diagnostic analysis Approach to Correlation Maps Through large Language Models

Diagnostic Analysis Approach to Correlation Maps Through Lar...

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ieee International Scientific Conference on Informatics

作者： Adam Dudáš Michal Vagač Department of Computer Science Faculty of Natural Sciences Matej Bel University Banská Bystrica Slovakia

ISBN: (数字)9798350387681

ISBN: (纸本)9798350387698

Diagnostic data analysis is a methodological process aimed at the identification of underlying causes of events, the effect of which is the presence of trends and patterns in a multidimensional dataset. Since in most cases this cause originates in the area described by the data itself, knowledge from the field of data analysis, computer science or statistics is rarely enough for the analyst to explain the patterns, and it is necessary to examine the field of data expertise itself. In this work, we focus on creating a model for diagnostic data analysis, which automatically describes naturally correlated pairs of attributes using a large language model and provides an output in the form of a Diagnostic Sheet visualization. The proposed concept is then experimentally verified in case studies on two multidimensional datasets focused on environmental variable sensor measurements and cubic graph properties.

关键词： Analytical models data analysis Correlation large language models data visualization Minimization Market research Natural language processing Object recognition Informatics

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Addressing class imbalance challenge in Semantic Segmentation of ALS data through performance analysis of RandLA-NET and PointNET ++ 3

Addressing class imbalance challenge in Semantic Segmentatio...

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3rd ieee India Geoscience and Remote Sensing symposium, InGARSS 2023

作者： Chauhan, Pyare Lal Vijaywargiya, Jayati Ramiya, Anandakumar M Indian Institute of Space Science and Technology Thiruvananthapuram India

ISBN: (纸本)9798350325591

Accurate and efficient semantic labeling of point clouds is critical for utilizing the LiDAR data for varied applications. The voluminous nature of data coupled with its unstructured nature pose a challenge to the research community in achieving the desired accuracy. Computational complexity also plays a pivotal role while dealing with the large point cloud datasets. Deep learning based labeling approaches have been gaining popularity, owing to the accuracy it can deliver. However, the accuracy of deep learning architecture is heavily dependent on the availability of a large amount of training dataset. The class imbalance affects the classification accuracy of minor classes. This paper proposes to compare RandLA-Net, an encoder-decoder based deep learning network with the PointNet++, a point-based deep learning network for point cloud classification of significantly represented and under-represented classes. The proposed methodology has been tested on the benchmark DALES dataset, which has notable class imbalance of urban objects. Results show that RandLA-NET performs better, with an increase of 13mean IoU in comparison to POINT-NET++. The paper further discusses the performances of both approaches for labeling major and minor classes. © 2023 ieee.

关键词： Semantic Segmentation

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2022 ieee/ACM 26th International symposium on Distributed Simulation and Real Time Applications, DS-RT 2022

2022 IEEE/ACM 26th International Symposium on Distributed Si...

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26th ieee/ACM International symposium on Distributed Simulation and Real Time Applications, DS-RT 2022

ISBN: (纸本)9781665497992

The proceedings contain 25 papers. The topics discussed include: Blue Danube: a large-scale, end-to-end synchronous, distributed data stream processing architecture for time-sensitive applications;a pedestrian movement model for 3D visualization in a driving simulation environment;enabling simulation interoperability between international standards in the space domain;the use of the ieee HLA standard to tackle interoperability issues between heterogeneous components;a distributed digital twin implementation of a hemodialysis unit aimed at helping prevent the spread of the Omicron COVID-19 variant;Cell-DEVS CO

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Design of a Lightweight Privacy Communication Network System for Medium Enterprises 8

Design of a Lightweight Privacy Communication Network System...

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8th ieee Information Technology and Mechatronics Engineering Conference, ITOEC 2025

作者： Suo, Ruixiang Qiao, Wenxuan Bai, Xinyang Zhang, Yuyang Dong, Ping Beijing Jiaotong University School of Electronic and Information Engineering Beijing China

ISBN: (纸本)9798331517915

Medium and large enterprises need to exchange confidential information frequently. Traditional encryption techniques can only protect the security of communication data, but the privacy of the communicating entities' identity information cannot be effectively protected. Currently, common solutions include virtual private networks (VPNs) and single/multiple agent-based forwarding mechanisms. However, the egress nodes of VPNs are susceptible to attacks and monitoring;agents in single-agent forwarding mechanisms are easy targets for single points of failure and attacks. Medium and large enterprises urgently need a solution that ensures communication privacy while being cost-effective and easy to manage. Therefore, this paper proposes a lightweight privacy communication network system based on a multi-agent mechanism to realize the non-association of communication parties and provide transparent transmission services. In order to schedule and manage the whole system flexibly, this paper implements a remote service and a visualization service in a Directory Server. In addition, an intelligent multi-objective optimization analysis of the route selection algorithm is introduced to ensure the anti-tracking and high efficiency of data transmission;meanwhile, traffic obfuscation technology is used to prevent attackers from recognizing the communication content and the identities of both parties through the traffic pattern, which improves the transmission and management security of the system. Test results show that the system effectively protects information communication relationships in medium and large enterprises. © 2025 ieee.

关键词： Intelligent multi-objective optimization analysis Multi-agent transmission Traffic obfuscation

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23rd International Conference on Web-Based Learning, ICWL 2024 was held in conjunction with the 9th International symposium on Emerging Technologies for Education, SETE 2024

23rd International Conference on Web-Based Learning, ICWL 20...

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23rd International Conference on Web-Based Learning, ICWL 2024 was held in conjunction with the 9th International symposium on Emerging Technologies for Education, SETE 2024

ISBN: (纸本)9789819644063

The proceedings contain 21 papers. The special focus in this conference is on Web-Based Learning. The topics include: Towards Effective Collaborative Learning in Edu-Metaverse: A Study on Learners’ Anxiety, Perception, and Behaviour;an Empirical Study of Code Pattern Reuse and Pattern Preference Consistency in Novice Programmers;self-attentive Knowledge Tracing with Relative Position Encoding;students’ Pre-concepts of Robot Control Before Their First Robotics Lesson: A Pilot Study;evaluating Verbs of Chatting in large Language Models;mapping the Landscape of Digital and Intelligent Technologies in Intercultural Learning: A Bibliometric analysis (2014–2024);AI-Enhanced Intercultural Teaching: Investigating Chinese EFL University Teachers’ Technology Readiness and Acceptance of large Language Models;leveraging data visualization with ggplot2 in Translation Pedagogy: Enhancing Learning Through Visual Insights;AI-Assisted Writing on News Report: A Case Study on AI-Generated Content and Human-Generated Content;analysis of Interpreter Performance on Numbers, Proper Nouns, and Complex Sentences in AI-Assisted English-Chinese Simultaneous Interpreting: An Eye-Tracking Experimental Study;Human v.s. AI: A Comparative Study Between MTI Student Translators and AI Engines;knowledge Graph-Based Advance Preparation in Medical Interpreting;Quality Comparison of MT in Translating Chinese Music Historical Texts and Inspiration for Translation Pedagogy;Human-in-the-Loop: A Conceptual Framework for Business English Teachers’ AI-Empowered Assessment;an Exploratory Research on Reading Comprehension Question Generation Based on Annotated Clause Complex Corpus;ChatGPT Prompt Engineering Methods for Financial News Translation;A Robust and Secure Framework for an AI-Assisted Academic Writing Platform;an Automatic Model for Lesson Plans Generation Based on Logical Chains and Prompt Tuning.

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Big data visualization analysis: Distribution of COVID-19 Mortality and Vaccination in the US 2

Big Data Visualization Analysis: Distribution of COVID-19 Mo...

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2nd International symposium on Electrical, Electronics and Information Engineering, ISEEIE 2022

作者： Meng, Bowen Cheng, Shenghui Kumar, Ayush Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China Chinese University of HongKong Shenzhen Shenzhen China Seri Harvard Medical School Boston United States

ISBN: (数字)9781665468749

ISBN: (纸本)9781665468749

The world has been experiencing the deadly disease COVID-19 since December 2019, and it has been studied extensively due to the availability of datasets in abundance. In this paper, we focus on mortality distribution and vaccination majorly as two major aspects of the COVID-19 dataset from the US. We compared the mortality and the vaccination distribution among states. We also discuss the correlation among various other aspects like race, party, region and age. © 2022 ieee.

关键词： COVID-19 Correlation data visualization Big data Vaccines Diseases

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Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration

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ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2022年第12期44卷 9830-9843页

作者： Yang, Shuo Wu, Songhua Liu, Tongliang Xu, Min Univ Technol Sydney Fac Engn & Informat Technol Sch Elect & Data Engn Ultimo NSW 2007 Australia Univ Sydney Sch Comp Sci Trustworthy Machine Learning Lab Darlington NSW 2008 Australia

A major gap between few-shot and many-shot learning is the data distribution empirically oserved by the model during training. In few-shot learning, the learned model can easily become over-fitted based on the biased distribution formed by only a few training examples, while the ground-truth data distribution is more accurately uncovered in many-shot learning to learn a well-generalized model. In this paper, we propose to calibrate the distribution of these few-sample classes to be more unbiased to alleviate such an over-fitting problem. The distribution calibration is achieved by transferring statistics from the classes with sufficient examples to those few-sample classes. After calibration, an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier. Specifically, we assume every dimension in the feature representation from the same class follows a Gaussian distribution so that the mean and the variance of the distribution can borrow from that of similar classes whose statistics are better estimated with an adequate number of samples. Extensive experiments on three datasets, miniImageNet, tieredImageNet, and CUB, show that a simple linear classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy by a large margin. Besides the favorable performance, the proposed method also exhibits high flexibility by showing consistent accuracy improvement when it is built on top of any off-the-shelf pretrained feature extractors and classification models without extra learnable parameters. The visualization of these generated features demonstrates that our calibrated distribution is an accurate estimation thus the generalization ability gain is convincing. We also establish a generalization error bound for the proposed distribution-calibration-based few-shot learning, which consists of the distribution assumption error, the distribution approximation error,

关键词： data models Task analysis Training Mathematical models Calibration Adaptation models Gaussian distribution Few-shot learning image classification transfer learning generalization error

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