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arXiv 2024年

作者： Duan, Bowen Chen, Shiming Guo, Yufei Xie, Guo-Sen Ding, Weiping Wang, Yisong The School of Computer Science and Technology Guizhou University Guizhou550025 China The Institute for Artificial Intelligence Guizhou University Guiyang China The Mohamed bin Zayed University of AI Abu Dhabi19282 United Arab Emirates The Intelligent Science & Technology Academy of CASIC Beijing China The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau 999078 China

Zero-shot learning (ZSL) aims to leverage additional semantic information to recognize unseen classes. To transfer knowledge from seen to unseen classes, most ZSL methods often learn a shared embedding space by simply aligning visual embeddings with semantic prototypes. However, methods trained under this paradigm often struggle to learn robust embedding space because they align the two modalities in an isolated manner among classes, which ignore the crucial class relationship during the alignment process. To address the aforementioned challenges, this paper proposes a Visual-Semantic Graph Matching Net, termed as VSGMN, which leverages semantic relationships among classes to aid in visual-semantic embedding. VSGMN employs a Graph Build Network (GBN) and a Graph Matching Network (GMN) to achieve two-stage visual-semantic alignment. Specifically, GBN first utilizes an embedding-based approach to build visual and semantic graphs in the semantic space and align the embedding with its prototype for first-stage alignment. Additionally, to supplement unseen class relations in these graphs, GBN also build the unseen class nodes based on semantic relationships. In the second stage, GMN continuously integrates neighbor and cross-graph information into the constructed graph nodes, and aligns the node relationships between the two graphs under the class relationship constraint. Extensive experiments on three benchmark datasets demonstrate that VSGMN achieves superior performance in both conventional and generalized ZSL scenarios. The implementation of our VSGMN and experimental results are available at github: https://***/dbwfd/VSGMN. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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A Machine Learning Model for the prediction of the progression of carotid arterial stenoses

A Machine Learning Model for the prediction of the progressi...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Panagiotis K. Siogkas Dimitrios S. Pleouras Vasilis D. Tsakanikas Vassiliki T. Potsika Kostas M. Tsiouris Antonis Sakellarios Evdokia Karamouzi Foteini Lagiou George Charalampopoulos George Galyfos Fragiska Sigala Igor Koncar Dimitrios I. Fotiadis Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece First Propaedeutic Dept. of Surgery National and Kapodistrian University of Athens Hippocration Hospital Athens Greece Department of Vascular and Endovascular Surgery Faculty of Medicine University of Belgrade Belgrade Serbia Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina and With the Biomedical Research Institute Forth University Campus of Ioannina Ioannina Greece

Atherosclerotic carotid plaque development results in a steady narrowing of the artery lumen, which may eventually trigger catastrophic plaque rupture leading to thromboembolism and stroke. The primary cause of ischemic stroke in the EU is carotid artery disease, which increases the demand for tools for risk stratification and patient management in carotid artery disease. Additionally, advancements in cardiovascular modeling over the past few years have made it possible to build accurate three-dimensional models of patient-specific primary carotid arteries. Computational models then incorporate the aforementioned 3D models to estimate either the development of atherosclerotic plaque or a number of flow-related parameters that are linked to risk assessment. This work presents an attempt to provide a carotid artery stenosis prognostic model, utilizing non-imaging and imaging data, as well as simulated hemodynamic data. The overall methodology was trained and tested on a dataset of 41 cases with 23 carotid arteries with stable stenosis and 18 carotids with increasing stenosis degree. The highest accuracy of 71% was achieved using a neural network classifier. The novel aspect of our work is the definition of the problem that is solved, as well as the amount of simulated data that are used as input for the prognostic *** Relevance—A prognostic model for the prediction of the trajectory of carotid artery atherosclerosis is proposed, which can support physicians in critical treatment decisions.

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Motion-to-Matching: A Mixed Paradigm for 3D Single Object Tracking

arXiv

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arXiv 2023年

作者： Li, Zhiheng Lin, Yu Cui, Yubo Li, Shuo Fang, Zheng Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

3D single object tracking with LiDAR points is an important task in the computer vision field. Previous methods usually adopt the matching-based or motion-centric paradigms to estimate the current target status. However, the former is sensitive to the similar distractors and the sparseness of point clouds due to relying on appearance matching, while the latter usually focuses on short-term motion clues (eg. two frames) and ignores the long-term motion pattern of target. To address these issues, we propose a mixed paradigm with two stages, named MTM-Tracker, which combines motion modeling with feature matching into a single network. Specifically, in the first stage, we exploit the continuous historical boxes as motion prior and propose an encoder-decoder structure to locate target coarsely. Then, in the second stage, we introduce a feature interaction module to extract motion-aware features from consecutive point clouds and match them to refine target movement as well as regress other target states. Extensive experiments validate that our paradigm achieves competitive performance on large-scale datasets (70.9% in KITTI and 51.70% in NuScenes). The code will be open soon at https://***/LeoZhiheng/ ***. Copyright © 2023, The Authors. All rights reserved.

关键词： Large dataset

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The Application of the New Pythagorean Fuzzy Entropy to Decision-Making using Linguistic Terms

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Procedia Computer science 2022年 207卷 4525-4534页

作者： Parul Thakur Aleksandra Kaczyńska Neeraj Gandotra Namita Saini Wojciech Sałabun Yogananda School of AI Computers and Data Science Shoolini University Solan HP INDIA Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Żołnierska 49 71-210 Szczecin POLAND

As a new conception of IFS (Intuitionistic Fuzzy Sets), Pythagorean Fuzzy Sets can manage conflicted details more fexibly in decision-making. This extension has been used repeatedly for decision making. This is an important trend in decision making that is worth further study in combination with the study of entropy properties. This paper proposes a new entropy for the Pythagorean Fuzzy Sets and an application measure using the TOPSIS method. The new entropy approach was used to estimate the objective weights in the decision-making procedure. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique is presumed that the optimum alternative has the least distance from the positive and farthest from the negative ideal solution. This approach has been widely adopted to solve MCDM issues in multiple fields. We present a simple study case where we use TOPSIS and new entropy to solve the group decision-making problem.

关键词： Entropy MCDM TOPSIS Pythagorean Fuzzy Sets Intuitionistic Fuzzy Sets

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Curriculum learning for vision-and-language navigation 21

Curriculum learning for vision-and-language navigation

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Proceedings of the 35th International Conference on Neural Information Processing systems

作者： Jiwen Zhang Zhongyu Wei Jianqing Fan Jiajie Peng School of Data Science Fudan University China School of Data Science Fudan University China and Research Institute of Intelligent and Complex Systems Fudan University China School of Data Science Fudan University China and Department of Operations Research and Financial Engineering Princeton University Research Institute of Intelligent and Complex Systems Fudan University China

ISBN: (纸本)9781713845393

Vision-and-Language Navigation (VLN) is a task where an agent navigates in an embodied indoor environment under human instructions. Previous works ignore the distribution of sample difficulty and we argue that this potentially degrade their agent performance. To tackle this issue, we propose a novel curriculum-based training paradigm for VLN tasks that can balance human prior knowledge and agent learning progress about training samples. We develop the principle of curriculum design and re-arrange the benchmark Room-to-Room (R2R) dataset to make it suitable for curriculum training. Experiments show that our method is model-agnostic and can significantly improve the performance, the generalizability, and the training efficiency of current state-of-the-art navigation agents without increasing model complexity.

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Distributed Optimal Load Frequency Control with Stochastic Wind Power Generation

Distributed Optimal Load Frequency Control with Stochastic W...

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2021 European Control Conference, ECC 2021

作者： Silani, Amirreza Cucuzzella, Michele Scherpen, Jacquelien M. A. Javad Yazdanpanah, Mohammad University of Groningen Jan C. Willems Center For Systems and Control ENTEG Faculty of Science and Engineering Groningen Netherlands University of Tehran Control Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Tehran Iran Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9789463842365

Motivated by the inadequacy of conventional control methods for power networks with a large share of renewable generation, in this paper we study the (stochastic) passivity property of wind turbines based on the Doubly Fed Induction Generator (DFIG). Differently from the majority of the results in the literature, where renewable generation is ignored or assumed to be constant, we model wind power generation as a stochastic process, where wind speed is described by a class of stochastic differential equations. Then, we design a distributed control scheme that achieves load frequency control and economic dispatch, ensuring the stochastic stability of the controlled network. © 2021 EUCA.

关键词： Random processes

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III-V/Si hybrid external-cavity laser utilizing TM mode in the passive photonic circuit 27

III-V/Si hybrid external-cavity laser utilizing TM mode in t...

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27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022

作者： Guo, Yuyao Li, Xinhang Jin, Minhui Lu, Liangjun Xie, Jingya Chen, Jianping Zhou, Linjie Shanghai Institute for Advanced Communication and Data Science State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation Center University of Shanghai for Science and Technology Shanghai Key Lab of Modern Optical System Shanghai200093 China

We present a III-V/Si hybrid laser using the silicon waveguide TM mode to reduce the propagation loss, backscattering, and two-photon absorption. The TM-mode laser has superior performance than the TE-mode laser. ... 详细信息

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Photons

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Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal data Fusion and Feature Extraction

arXiv

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arXiv 2024年

作者： Sun, Chuike Chen, Junzhou Zhao, Yue Han, Hao Jing, Ruihai Tan, Guang Wu, Di School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University No. 66 Gongchang Road Guangming District Guangdong Shenzhen518107 China Transsion Holdings Co. Ltd Shanghai201203 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangdong510006 China

This article presents Appformer, a novel mobile application prediction framework inspired by the efficiency of Transformer-like architectures in processing sequential data through self-attention mechanisms. Combining a Multi-Modal data Progressive Fusion Module with a sophisticated Feature Extraction Module, Appformer leverages the synergies of multimodal data fusion and data mining techniques while maintaining user privacy. The framework employs Points of Interest (POIs) associated with base stations, optimizing them through comprehensive comparative experiments to identify the most effective clustering method. These refined inputs are seamlessly integrated into the initial phases of cross-modal data fusion, where temporal units are encoded via word embeddings and subsequently merged in later stages. The Feature Extraction Module, employing Transformer-like architectures specialized for time series analysis, adeptly distils comprehensive features. It meticulously fine-tunes the outputs from the fusion module, facilitating the extraction of high-calibre, multi-modal features, thus guaranteeing a robust and efficient extraction process. Extensive experimental validation confirms Appformer’s effectiveness, attaining state-of-the-art (SOTA) metrics in mobile app usage prediction, thereby signifying a notable progression in this field. © 2024, CC BY-NC-SA.

关键词： Spatio-temporal data

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A Novel Agricultural data Sharing Mode Based on Rice Disease Identification

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Plant Diseases and Pests 2024年第2期15卷 9-16页

作者： Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning *** demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model ***,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial *** issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data *** address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

关键词： Rice disease and pest identification Convolutional neural networks Distributed training Federated learning(FL) Open-source data sharing platform

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Comments on "An Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks"

Comments on "An Efficient and Provably Secure Authenticated ...

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IEEE International Conference on Electronics and Communication engineering (ICECE)

作者： Syed Muhammad Awais Wu Yucheng Khalid Mahmood Rupak Kharel School of Microelectronics and Communication Engineering Chongqing University Chongqing China School of Psychology and Computer Science and Graduate School of Intelligent Data Science University of Central Lancashire and National Yunlin University of Science and Technology Preston United Kingdom Yunlin Taiwan School of Psychology and Computer Science and Faculty of Electrical and Electronics Engineering University of Central Lancashire and Ton Duc Thang University Preston United Kingdom Ho Chi Minh City Vietnam

Vehicle ad-hoc networks (VANETs) have experienced rapid growth due to the advancement of cloud computing, IoT technologies, and intelligent transportation systems (ITS). Vehicles are required to have enhanced storage capacity, onboard computing capabilities, improved sensing power, and communication systems. To address real-world demands like low latency, affordable storage, and mobility in VANET deployments, There have been efforts to integrate fog computing with VANETs in a practical implementation. "An Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks" was proposed by Ma et al. (IEEE Internet of Things Journal, pp 8065–8075, 10.1109/JIOT.2019.2902840). According to their claims, the use of their secure authentication technique can help prevent security threats. However, after careful investigation, we discovered that their authentication protocol is susceptible to vehicle user impersonation attacks and also does not provide vehicle anonymity. In light of this, we have provided some recommendations to address the current flaws in the protocol developed by Ma et al.

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