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FlexSATE: Flexible and Distributed Traffic Engineering with Supervised Learning in Ultra-Dense Low-Earth-Orbit Satellite Networks

FlexSATE: Flexible and Distributed Traffic Engineering with ...

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2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Liu, Xiaoyu Zhang, Zitian Qin, Xiaohan Zhou, Haibo Zhao, Lian Nanjing University School of Electronic Science and Engineering Nanjing210023 China Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou China Toronto Metropolitan University Department of Electrical Computer Biomedical Engineering TorontoONM5B 2K3 Canada

ISBN: (纸本)9798350351255

The ultra-dense low earth orbit (UD-LEO) satellite network is being vigorously developed due to its great potential in providing global coverage and services. For the sake of improved network performance in resource-constrained satellite networks, multipath schemes are being explored. However, state-of-the-art multipath routing algorithms face the challenge when dealing with highly dynamic satellite network features (i.e., frequent traffic variation, link failures) and fail to exploit the simple grid topology to design fast yet efficient traffic engineering (TE) approaches. In this paper, we propose a novel distributed TE scheme called Flexible Satellite Traffic Engineering (FlexSATE), which leverages global path computation coupled with distributed local routing decisions to improve the overall load balancing performance for ultra-dense LEO satellite networks. By constructing a minimum-hop binary tree (MHBT), we propose an MHBT-based k-segment Routing algorithm, which is capable of promptly discovering routing paths with low latency, high diversity, and good load balancing. To further enhance network transmission performance, we employ supervised learning into dynamic rate adaption, where FlexSATE employs centralized offline learning to derive insights from the globally optimal routing strategy and utilizes distributed deployment to predict the optimal distribution of traffic in real time. Our simulation results on a real-world typical Walker-delta type LEO constellation with 720 satellites show that FlexSATE outperforms some existing approaches with superior robustness and flexibility. © 2024 IEEE.

关键词： Binary trees

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Comparison of self-supervised learning methods for optical coherence tomography image classification 24

Comparison of self-supervised learning methods for optical c...

引用

8th International conference on Medical and Health Informatics, ICMHI 2024

作者： Shim, Sungho Yang, Hyun-Lim Kim, Min-Soo Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science and Technology Daegu Korea Republic of Office of Hospital Information Seoul National University Hospital Seoul Korea Republic of School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9798400716874

Self-supervised learning enables deep learning algorithms to achieve high performance even with limited labeled data. Numerous self-supervised learning methods have been proposed, and several medical artificial intelligence studies have confirmed the effectiveness of this technique in enhancing model performance. Nonetheless, only a few studies have investigated which self-supervised learning methods are appropriate for medical images. In this study, a comparative analysis to determine the most suitable self-supervised learning methods for developing medical artificial intelligence models using optical coherence tomography images. Four self-supervised learning methods were tested, and models were developed and validated using two publicly available optical coherence tomography datasets. The reliability of the model output was assessed through Gradient-weighted Class Activation Mapping analysis. As a result, using contrastive learning-based self-supervised learning methods, better performance was observed compared to other models, resulting in 99.90% and 99.05% accuracy for the two public optical coherence tomography datasets, which represents a 0.8% and 2.86% improvement over conventional supervised learning training. In addition, we confirmed that the contrastive learning-based model can more reliably localize disease lesions in the Gradient-weighted Class Activation Mapping analysis. © 2024 Copyright held by the owner/author(s)

关键词： Self-supervised learning

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Representing the Intangible Cultural Heritage Knowledge Graph with Vector Embedding 25

Representing the Intangible Cultural Heritage Knowledge Grap...

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25th IEEE International conferences on high performance Computing and Communications, 9th International conference on Data Science and Systems, 21st IEEE International conference on Smart City and 9th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Han, Mao Wang, Qing Chen, Hong Chen, Wei Zhang, Junchang Wang, Guang College of Information and Electrical Engineering China Agricultural University Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China Century College Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350330014

The construction and effective application of an intangible cultural heritage knowledge graph (ICH KG) can realize the knowledge integration, and optimize the ICH knowledge management. However, high complexity and low computational efficiency of ICH KG make its application face challenges. We propose a multi-source knowledge graph embedding (KGE) model named ICHMKGE to convert the ICH KG into the vector representations to improve the computational efficiency of ICH KG and promote the digital sustainable development of ICH. Firstly, we take the Chinese ICH project 24 solar terms as an example and combine multiple official data sources to construct the Chinese ICH KG as a basis for this study. Secondly, as the ICH project is being further explored with the limited coverage of ICH knowledge, entity sparsity poses a serious challenge for ICH KGE. This paper employs the BERT model to encode the complete description information of entities, and establishes connection between triples entities and ontology concepts via cross-view modeling. Finally, the proposed ICHMKGE model is compared with the baseline models, and the experimental results demonstrate that the model exhibits superiority. © 2023 IEEE.

关键词： Vector spaces

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Image-Adaptive Hint Generation via Vision Transformer for Outpainting 22

Image-Adaptive Hint Generation via Vision Transformer for Ou...

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22nd IEEE/CVF Winter conference on Applications of computer Vision (WACV)

作者： Kong, Daehyeon Kong, Kyeongbo Kim, Kyunghun Min, Sung-Jun Kang, Suk-Ju Sogang Univ Seoul South Korea Pukyong Natl Univ Busan South Korea

ISBN: (纸本)9781665409155

Image outpainting has recently received considerable attention because it can be useful in tasks such as image retargeting and panorama image generation. In general, the problem of extending an image beyond its given boundaries is still ill-posed. Conventional methods predominantly attempt image outpainting by using complex network structures. Some recent studies have tried to decrease the problem complexity through the conversion techniques from outpainting to inpainting. Although these methodologies work well in simple cases, their performance reduces considerably for asymmetrical images. This paper proposes a novel hint-based outpainting methodology that can adaptively select the most plausible patches as hints from a given image to reduce the difficulty of outpainting. To estimate high-quality hints, inspired by patch-based image inpainting methods, we utilize Vision Transformer that considers self-attention for each patch. The estimated hints are attached on both boundaries of the input image and the inside missing regions are predicted by using an inpainting network. After finishing the prediction, the output image is obtained by removing the hints. Experiments show that our image-adaptive hint framework, when employed in representative inpainting networks, can consistently improve its performance compared to the other conversion techniques from outpainting to inpainting on SUN and Beach benchmark datasets.

关键词： computer vision Image synthesis Neural networks Complex networks Benchmark testing Transformers Complexity theory

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Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis 30

Learning Intrinsic Dimension via Information Bottleneck for ...

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Joint 30th International conference on Computational Linguistics and 14th International conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Cheng, Zhenxiao Zhou, Jie Wu, Wen Chen, Qin He, Liang School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9782493814104

Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) due to their high fidelity. Such methods determine word-level importance using dimension-level gradient values through a norm function, often presuming equal significance for all gradient dimensions. However, in the context of Aspect-based Sentiment Analysis (ABSA), our preliminary research suggests that only specific dimensions are pertinent. To address this, we propose the Information Bottleneck-based Gradient (IBG) explanation framework for ABSA. This framework leverages an information bottleneck to refine word embeddings into a concise intrinsic dimension, maintaining essential features and omitting unrelated information. Comprehensive tests show that our IBG approach considerably improves both the models' performance and interpretability by identifying sentiment-aware features. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness 24

VeCAF: Vision-language Collaborative Active Finetuning with ...

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32nd ACM International conference on Multimedia, MM 2024

作者： Zhang, Rongyu Cai, Zefan Yang, Huanrui Liu, Zidong Gudovskiy, Denis Okuno, Tomoyuki Nakata, Yohei Keutzer, Kurt Chang, Baobao Du, Yuan Du, Li Zhang, Shanghang Nanjing University Nanjing China Peking University Beijing China University of Wisconsin - Madison MadisonWI United States University of California Berkeley BerkeleyCA United States University of Arizona Tucson United States Tsinghua University Beijing China Panasonic Corporation Mountainview United States Panasonic Corporation Osaka Japan State Key Laboratory of Multimedia Information Processing School of Computer Science Peking University China

ISBN: (纸本)9798400706868

Finetuning a pretrained vision model (PVM) is a common technique for learning downstream vision tasks. The conventional finetuning process with the randomly sampled data points results in diminished training efficiency. To address this drawback, we propose a novel approach, Vision- languag e C ollaborative A ctive F inetuning (VeCAF). VeCAF optimizes a parametric data selection model by incorporating the training objective of the model being tuned. Effectively, this guides the PVM towards the performance goal with improved data and computational *** the ever-growing feasibility of acquiring labels and natural language annotations of image data through web-scale crawling, we exploit the inherent semantic richness of the text embedding space and utilize text embeddings of image annotations to augment PVM image features for better data selection and finetuning. Furthermore, the flexibility of text-domain augmentation gives VeCAF the unique ability to handle out-of-distribution scenarios without external augmented data. Extensive experiments show the leading performance and high efficiency of VeCAF that is superior to baselines in both in-distribution and out-of-distribution image classification tasks. On ImageNet, VeCAF needs up to 3.3× less training batches to reach the target performance compared to full fine-tuning and achieves an accuracy improvement of 2.8% over active SOTA fine-tuning methods with the same number of batches. Our code is now available at https://***/RoyZry98/VeCAF-Pytorch. © 2024 ACM.

关键词： Semantics

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Enabling Lightweight Privilege Separation in Applications with MicroGuards 21st

Enabling Lightweight Privilege Separation in Applications wi...

引用

21st International conference on Applied Cryptography and Network Security (ACNS)

作者： Tarkhani, Zahra Madhavapeddy, Anil Microsoft Res Cambridge Cambridge England Univ Cambridge Cambridge England

ISBN: (纸本)9783031411809;9783031411816

Application compartmentalization and privilege separation are our primary weapons against ever-increasing security threats and privacy concerns on connected devices. Despite significant progress, it is still challenging to privilege separate inside an application address space and in multithreaded environments, particularly on resource-constrained and mobile devices. We propose MicroGuards, a lightweight kernel modification and set of security primitives and APIs aimed at flexible and fine-grained in-process memory protection and privilege separation in multithreaded applications. MicroGuards take advantage of hardware support in modern CPUs and are high-level enough to be adaptable to various architectures. This paper focuses on enabling MicroGuards on embedded and mobile devices running Linux kernel and utilizes tagged memory support to achieve good performance. Our evaluation show that MicroGuards add small runtime overhead (less than 3.5%), minimal memory footprint, and are practical to get integrated with existing applications to enable fine-grained privilege separation.

关键词： program processors

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An Education program for Data Driven Security with Machine Learning Applications

An Education Program for Data Driven Security with Machine L...

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IEEE International conference on Big Data Security on Cloud (BigDataSecurity), high performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS)

作者： Bhavani Thuraisingham Kangkook Jee Latifur Khan The University of Texas Dallas

This paper discusses our plan for an education program for data driven security for machine learning applications. We discuss the motivation for such a program, details of the program, various course modules to be dev... 详细信息

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

关键词： Education Machine learning Data science Big Data Security computer crime

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medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs 31

medIKAL: Integrating Knowledge Graphs as Assistants of LLMs ...

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31st International conference on Computational Linguistics, COLING 2025

作者： Jia, Mingyi Duan, Junwen Song, Yan Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University China University of Science and Technology of China China

ISBN: (纸本)9798891761964

Electronic Medical Records (EMRs), while integral to modern healthcare, present challenges for clinical reasoning and diagnosis due to their complexity and information redundancy. To address this, we proposed medIKAL (Integrating Knowledge Graphs as Assistants of LLMs), a framework that combines Large Language Models (LLMs) with knowledge graphs (KGs) to enhance diagnostic capabilities. medIKAL assigns weighted importance to entities in medical records based on their type, enabling precise localization of candidate diseases within KGs. It innovatively employs a residual network-like approach, allowing initial diagnosis by LLMs to be merged into KG search results. Through a path-based reranking algorithm and a fill-in-the-blank style prompt template, it further refined the diagnostic process. We validated medIKAL's effectiveness through extensive experiments on a newly introduced open-sourced Chinese EMR dataset, demonstrating its potential to improve clinical diagnosis in real-world settings. The code and dataset are publicly available at https://***/CSU-NLP-Group/mediKAL. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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BiHPF: Bilateral high-Pass Filters for Robust Deepfake Detection 22

BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detec...

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22nd IEEE/CVF Winter conference on Applications of computer Vision (WACV)

作者： Jeong, Yonghyun Kim, Doyeon Min, Seungjai Joe, Seongho Gwon, Youngjune Choi, Jongwon Samsung SDS Seoul South Korea Chung Ang Univ Dept Adv Imaging Seoul South Korea

ISBN: (纸本)9781665409155

The advancement in numerous generative models has a two fold effect: a simple and easy generation of realistic synthesized images, but also an increased risk of malicious abuse of those images. Thus, it is important to develop a generalized detector for synthesized images of any GAN model or object category, including those unseen during the training phase. However, the conventional methods heavily depend on the training settings, which cause a dramatic decline in performance when tested with unknown domains. To resolve the issue and obtain a generalized detection ability, we propose Bilateral high-Pass Filters (BiHPF), which amplify the effect of the frequency-level artifacts that are generally found in the synthesized images of generative models. Also, to find the properties of the general frequency-level artifacts, we develop an additional method to adversarially extract the artifact compression map. Numerous experimental results validate that our method outperforms other state-of-the-art methods, even when tested with unseen domains.

关键词： Training Frequency synthesizers Image resolution Image coding Image color analysis Computational modeling Data models

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