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IEEE International Conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Nitin Dabi Mohit P. Tahiliani Vishal J. Rathod Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Mangalore Karnataka India Internet of Things - Research and Development Group Centre for Development of Advanced Computing (C-DAC) Bengaluru Karnataka India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Transport Layer Security (TLS) is the standard protocol widely used for securing client-server communications over the Internet. It ensures the confidentiality and integrity of data transmitted between systems. With the rising deployment of TLS 1.3, which is the latest version of the protocol, stronger encryption and improved security features are introduced. The adoption of TLS 1.3 brings about the need for techniques such as TLS fingerprinting. TLS fingerprinting involves analyzing the unique characteristics of TLS handshakes to identify and classify different implementations or configurations of TLS. However, researchers currently lack a dedicated platform to test TLS and TLS fingerprinting techniques in a controlled environment. To address this gap, we aim to enhance NeST (Network Stack Tester) by adding support for TLS and TLS fingerprinting. NeST is a Python tool for network emulation that internally uses Linux network namespaces and Linux utilities to create virtual testbeds, perform experiments using various network protocols and collect statistics. By integrating TLS and TLS fingerprinting into NeST, we provide an easy-to-use tool that enables researchers to experiment with TLS, analyze TLS traffic, and evaluate the effectiveness of TLS fingerprinting techniques.

关键词： Protocols Linux Emulation Fingerprint recognition Communications technology Internet Encryption

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research and Optimization of Electrode-Assisted Human Key Point Detection Algorithm Based on Channel Attention and Adaptive Feature Fusion

Research and Optimization of Electrode-Assisted Human Key Po...

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International Conference on Computer and Communications (ICCC)

作者： Hongliang Wang Jing Xu Xiaoxing Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China

In carotid endarterectomy surgery, it is crucial for the surgeon to accurately locate the stenosis. Due to the medical application of this method and the actual research cost, Electrode, which is commonly used in medical fields, was chosen as the detection target in this study. To address the above issues, this paper proposes an Electrode-assisted human key point detection algorithm based on channel attention and adaptive feature fusion, and conducts experiments using a self-labeled dataset. To improve the detection accuracy in complex scenes and complete a large amount of computation in a short time, a series of techniques were employed, including using a deeper convolutional neural network, a larger receptive field, and more feature layers to enhance detection accuracy, fusing convolutional layers, using FPN and other acceleration techniques to improve detection speed. The channel attention mechanism can adaptively learn the importance of each channel, thereby capturing key information in the image better and improving the model's generalization performance. The adaptive feature fusion can adaptively learn the weight of each feature map, thereby better fusing feature information from different levels and further improving the model's generalization performance. The experiment shows that the average accuracy of this method on the dataset is 89%.

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Non-Gaussian state generation by multi-photon subtraction at the telecommunication wavelength

Non-Gaussian state generation by multi-photon subtraction at...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Endo, Mamoru He, Ruofan Sonoyama, Tatsuki Takahashi, Kazuma Kashiwazaki, Takahiro Umeki, Takeshi Takasu, Sachiko Hattori, Kaori Fukuda, Daiji Fukui, Kosuke Takase, Kan Asavanant, Warit Yoshikawa, Jun-Ichi Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan NTT Device Technology Labs. NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan National Institute of Advanced Industrial Science and Technology Ibaraki Tsukuba305-8563 Japan AISTU Tokyo Advanced Operando-Measurement Technology Open Innovation Laboratory Ibaraki Tsukuba305-8563 Japan

Non-Gaussian quantum states were generated by photon subtraction of up to four photons from a squeezed light at 1545.32 nrn. The Wigner negativity and the parity change with the subtracted photon number were directly ... 详细信息

ISBN: (纸本)9781957171258

关键词： Photons

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Context-aware Multimodal AI Reveals Hidden Pathways in Five Centuries of Art Evolution

arXiv

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arXiv 2025年

作者： Kim, Jin Lee, Byunghwee You, Taekho Yun, Jinhyuk Department of Intelligent Semiconductors Soongsil University Seoul06978 Korea Republic of School of Digital Humanities & Computational Social Sciences Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Institute for Social Data Science Pohang University of Science and Technology Pohang37673 Korea Republic of Center for Digital Humanities & Computational Social Sciences Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of School of AI Convergence Soongsil University Seoul06978 Korea Republic of

The rise of multimodal generative AI is transforming the intersection of technology and art, offering deeper insights into large-scale artwork. Although its creative capabilities have been widely explored, its potential to represent artwork in latent spaces remains underexamined. We use cutting-edge generative AI, specifically Stable Diffusion, to analyze 500 years of Western paintings by extracting two types of latent information with the model: formal aspects (e.g., colors) and contextual aspects (e.g., subject). Our findings reveal that contextual information differentiates between artistic periods, styles, and individual artists more successfully than formal elements. Additionally, using contextual keywords extracted from paintings, we show how artistic expression evolves alongside societal changes. Our generative experiment, infusing prospective contexts into historical artworks, successfully reproduces the evolutionary trajectory of artworks, highlighting the significance of mutual interaction between society and art. This study demonstrates how multimodal AI expands traditional formal analysis by integrating temporal, cultural, and historical contexts. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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StereoVAE: A lightweight stereo-matching system using embedded GPUs

StereoVAE: A lightweight stereo-matching system using embedd...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Qiong Chang Xiang Li Xin Xu Xin Liu Yun Li Jun Miyazaki School of Computing Tokyo Institute of Technology Tokyo Japan School of Electronic Science & Engineering Nanjing University Nanjing China Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan

We propose a lightweight system for stereo-matching using embedded graphic processing units (GPUs). The proposed system overcomes the trade-off between accuracy and processing speed in stereo matching, thus further improving the matching accuracy while ensuring real-time processing. The basic idea is to construct a tiny neural network based on a variational autoencoder (VAE) to achieve the upscaling and refinement a small size of coarse disparity map. This map is initially generated using a traditional matching method. The proposed hybrid structure maintains the advantage of low computational complexity found in traditional methods. Additionally, it achieves matching accuracy with the help of a neural network. Extensive experiments on the KITTI 2015 benchmark dataset demonstrate that our tiny system exhibits high robustness in improving the accuracy of coarse disparity maps generated by different algorithms, while running in real-time on embedded GPUs.

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Climatization and luminosity optimization of buildings using genetic algorithm, random forest, and regression models

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Energy Informatics 2021年第2期4卷 1-18页

作者： Mota, Bruno Albergaria, Miguel Pereira, Helder Silva, José Gomes, Luis Vale, Zita Ramos, Carlos Institute of Engineering - Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Rua Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

With the rise in popularity of artificial intelligence, coupled with the growing concern over the environment, there has been a surge in the use of intelligent energy management systems. Additionally, as more buildings transition into the smart grid and, consequently, more energy and environmental data is gathered, there has been a significant increase in the number of data-driven approaches for building management systems. This paper proposes a methodology that aims to optimize the climatization and luminosity inside a building, using a genetic algorithm, a random forest, and two polynomial models. The proposed methodology enables the real-time management of the building taking into account the user needs and preferences. Air conditioner units and light systems are optimized to minimize energy costs, while also improving the air quality and considering the users’ temperature and luminosity preferences. This paper shows the results achieved, by the proposed solution, in an office building case study. The promising results demonstrate the possibility of minimizing energy costs while maximizing the users’ comfort. © 2021, The Author(s).

关键词： Air quality

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Comparison of Machine Learning Models for Injury Prediction in Athletes

Comparison of Machine Learning Models for Injury Prediction ...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Murugan, Suganiya Kumar, S. Pradeep Kalaiarasi, G. Saritha, B. Rubini, B. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Vels Institute of Science Technology and Advanced Studies Department of Eee Tamil Nadu Chennai India Vignan Foundation for Science Technology and Research Department of Acse Andhrapradesh India Erode Sengunthar Engineering College Thudupathi Erode Department of Biomedical Engineering Tamil Nadu India Vels Institute of Science Technology and Advanced Studies Department of Eee TamilNadu Chennai India

ISBN: (纸本)9798331521394

Running is a favoured exercise for millions, but it carries a risk of musculoskeletal injuries, particularly to the lower extremities. Identifying and predicting potential injuries is crucial for prevention. This study explores the use of machine learning techniques to predict injuries among runners, leveraging data on demographics, running habits, health conditions, and previous injuries. Using models like Random Forest, K-Nearest Neighbours, Decision Trees, and Logistic Regression, the study examines the relevance of various attributes and assesses the predictive power of these models. Remarkably, our model achieved an impressive accuracy of over 99%, setting a new benchmark for predictive precision in injury prevention. Visualizations provide insight into attribute interactions and their influence on injury risk, facilitating the development of targeted prevention strategies. © 2025 IEEE.

关键词： Logistic regression

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APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigation

arXiv

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arXiv 2024年

作者： Xu, Zikang Tang, Fenghe Quan, Quan Yao, Qingsong Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Institute of Computing Technology CAS Beijing100086 China

Ensuring fairness in deep-learning-based segmentors is crucial for health equity. Much effort has been dedicated to mitigating unfairness in the training datasets or procedures. However, with the increasing prevalence of foundation models in medical image analysis, it is hard to train fair models from scratch while preserving utility. In this paper, we propose a novel method, Adversarial Privacy-aware Perturbations on Latent Embedding (APPLE), that can improve the fairness of deployed segmentors by introducing a small latent feature perturber without updating the weights of the original model. By adding perturbation to the latent vector, APPLE decorates the latent vector of segmentors such that no fairness-related features can be passed to the decoder of the segmentors while preserving the architecture and parameters of the segmentor. Experiments on two segmentation datasets and five segmentors (three U-Net-like and two SAM-like) illustrate the effectiveness of our proposed method compared to several unfairness mitigation methods. © 2024, CC BY.

关键词： Embeddings

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Towards All-optical Cavity-based Memory for Non-Gaussian Quantum State engineering

Towards All-optical Cavity-based Memory for Non-Gaussian Qua...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Hanamura, Fumiya Takase, Kan Hirota, Kazuki Nehra, Rajveer Lang, Florian Miki, Shigehito Terai, Hirotaka Yabuno, Masahiro China, Fumihiro Kahiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Asavanant, Warit Endo, Mamoru Yoshikawa, Jun-Ichi Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts-Amherst AmherstMA01003 United States Department of Physics University of Massachusetts-Amherst AmherstMA01003 United States Department of Information Technology and Electrical Engineering ETH Zurich Zurich8092 Switzerland Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Hyogo Kobe651-2492 Japan Graduate School of Engineering Kobe University 1-1 Rokkodai-cho Nada-ku Hyogo Kobe657-0013 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan

We present our experimental results on all-optical memory to store and manipulate quantum states, and propose resource-efficient time-domain-multiplexed state engineering using the memory. This paves a path to scalabl... 详细信息

ISBN: (纸本)9781957171395

关键词： Optical recording

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Web-based platform for the management of citizen energy communities and their members

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Energy Informatics 2021年第2期4卷 1-17页

作者： Pereira, Helder Gomes, Luis Faria, Pedro Vale, Zita Coelho, Carlos GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Rua Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal Institute of Engineering - Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

The appearance of citizen energy communities demands the conception, development, and testing of new management models for the community and its end-users. Citizen energy communities promote the active participation of end-users, including them in the management of the community. End-users are incentivized to participate in demand response programs and share their energy among peers, enabling a decrease in their energy costs. In this paper, it is proposed a platform for the management of citizen energy communities. The paper focuses and presents four services related to energy tariffs, end-users’ aggregation, price elasticity, and load response. The services are based on historical data and enable deep analysis of end-users’ energy profiles. As the platform allows the upload of different scenarios, it is possible to test and validate management models in multiple energy communities and scenarios and study their impact in different conditions. The paper presents a case study, where all the services are applied to a community with 996 end-users. © 2021, The Author(s).

关键词： Elasticity

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