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International Conference on Computer Supported Cooperative Work in Design

作者： Zinuo Zhang Ruoting Xiong Xinyu Di Wei Ren School of Computer Science China University of Geosciences Wuhan China School of Computer Science University of East Anglia Norwich UK International College Chongqing University of Technology Chongqing China Ministry of Education Key Laboratory of Data Protection and Intelligent Management Sichuan University Chengdu China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Current solutions on cross-domain access authentication based on blockchain are largely using digital certificate authentication, where users cannot control their identity, and some operations, such as certificate authentication and update, are inefficient. In this paper, we propose a Self-Sovereign Identity (SSI) based cross-domain authentication scheme implemented on a consortium chain. SSI is introduced as a decentralized digital identity solution to enable user-controlled identity management. Besides, blockchain, as an infrastructure for distributed digital identity, provides a storage place for decentralized identity identifiers and related data. In our scheme, users only need to register in one domain, and can use verifiable credentials (VC) to access other domains in the same consortium chain, without repeated registration. We also propose key method for quickly changing accounts in case of account loss. The logic, performance and security analysis justified that our scheme can protect the privacy of users and can remain feasible in case of a large scale of users.

关键词： Privacy Federated learning Computational modeling Buildings Authentication Blockchains Registers

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Deepdualmapper: A gated fusion network for automatic map extraction using aerial images and trajectories

arXiv

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arXiv 2020年

作者： Wu, Hao Zhang, Hanyuan Zhang, Xinyu Sun, Weiwei Zheng, Baihua Jiang, Yuning School of Computer Science Fudan University Systems and Shanghai Key Laboratory of Data Science Fudan University Shanghai Insitute of Intelligent Electroics & Systems Singapore Management University Bytedance AI Lab

Automatic map extraction is of great importance to urban computing and location-based services. Aerial image and GPS trajectory data refer to two different data sources that could be leveraged to generate the map, although they carry different types of information. Most previous works on data fusion between aerial images and data from auxiliary sensors do not fully utilize the information of both modalities and hence suffer from the issue of information loss. We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map. We design a gated fusion module to explicitly control the information flows from both modalities in a complementary-aware manner. Moreover, we propose a novel densely supervised refinement decoder to generate the prediction in a coarse-to-fine way. Our comprehensive experiments demonstrate that DeepDualMapper can fuse the information of images and trajectories much more effectively than existing approaches, and is able to generate maps with higher accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Extraction

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Reinforcement Learning Methods for Fixed-Wing Aircraft Control

Reinforcement Learning Methods for Fixed-Wing Aircraft Contr...

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International Conference on Systems and Informatics (ICSAI)

作者： Lun Li Xuebo Zhang Yang Wang Chenxu Qian Minghui Zhao College of Artificial Intelligence Nankai University Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics Tianjin China Tianjin Municipal Data Management Center Tianjin China

In recent years, reinforcement learning (RL) has been widely researched for fixed-wing aircraft control, offering transformative potential for more adaptive, efficient, and autonomous flight operations. This paper presents a comprehensive analysis of the current state, advantages, challenges, and future prospects of employing RL in fixed-wing aircraft control. The advantages of RL include model-free, adaptive decision-making, and effectively solving complex nonlinear constraints, leading to increased efficiency and maneuverability in high-dynamic environments. However, significant challenges, such as the guarantee of stability and optimization, interpretability, and sim-to-real transferring, highlight areas necessitating further attention and development. Overcoming these challenges requires collaborative efforts among aviation experts, control theory experts, and machine learning researchers to ensure safe, reliable integration of RL in flight systems. As research and advancements continue, the RL holds promise for providing a more efficient, maneuverable, and autonomous strategy for controlling fixed-wing aircraft in the future.

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Modeling Route Representation With Mixed-Scale Hierarchical Transformer

Modeling Route Representation With Mixed-Scale Hierarchical ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hanyuan Zhang Yuqi Chen Xinyu Zhang Qize Jiang Liang Li Baihua Zheng Weiwei Sun School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Fudan University Shanghai Institute of Intelligent Electronics & Systems School of Computing and Information Systems Singapore Management University

Modeling route representation aims to obtain contextual representations of an entire route for various traffic-related tasks. In reality, spatial-temporal data often exhibits multi-scale characteristics, which are utilized by many studies to enhance their performance. However, there is still a lack of in-depth research on how to effectively incorporate the multi-scale spatial-temporal information into transformer structure to adequately model route representation. In this paper, we propose a novel hierarchical route representation framework called RouteMT, which effectively captures multi-scale spatial-temporal characteristics of routes and leverages a mixed-scale transformer architecture to fuse intra and interroute features. Experiments on real data confirm RouteMT’s superior performance and versatility.

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Partial Relaxed Optimal Transport for Denoised Recommendation

Partial Relaxed Optimal Transport for Denoised Recommendatio...

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2022 Workshop on Deep Learning for Search and Recommendation, DL4SR 2022

作者： Tan, Yanchao Yang, Carl Wei, Xiangyu Wu, Ziyue Liu, Weiming Zheng, Xiaolin The College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China The Department of Computer Science Emory University Atlanta30322 United States The College of Computer Science Zhejiang University Hangzhou310027 China The School of Management Zhejiang University Hangzhou310058 China

The interaction data used by recommender systems (RSs) inevitably include noises resulting from mistaken or exploratory clicks, especially under implicit feedbacks. Without proper denoising, RS models cannot effectively capture users’ intrinsic preferences and the true interactions between users and items. To address such noises, existing methods mostly rely on auxiliary data which are not always available. In this work, we ground on Optimal Transport (OT) to globally match a user embedding space and an item embedding space, allowing both non-deep and deep RS models to discriminate intrinsic and noisy interactions without supervision. Specifically, we firstly leverage the OT framework via Sinkhorn distance to compute the continuous many-to-many user-item matching scores. Then, we relax the regularization in Sinkhorn distance to achieve a closed-form solution with a reduced time complexity. Finally, to consider individual user behaviors for denoising, we develop a partial OT framework to adaptively relabel user-item interactions through a personalized thresholding mechanism. Extensive experiments show that our framework can significantly boost the performances of existing RS models. © 2022 Copyright for this paper by its authors.

关键词： Embeddings

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Classification Based on Multilayer Extreme Learning Machine for Motor Imagery Task from EEG Signals

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Procedia Computer Science 2016年 88卷 176-184页

作者： Lijuan Duan Menghu Bao Jun Miao Yanhui Xu Juncheng Chen College of Computer Science and Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing 100124 China

Classification of motor imagery electroencephalogram (EEG) is one of the most important technologies for BCI. To improve the accuracy, this paper introduces a classification system based on Multilayer Extreme Learning Machine (ML-ELM). In the system, the combination of PCA and LDA is chosen as the method of feature extraction and the ML-ELM is used to classify. The ML-ELM has not only the advantage which ELM has but also better performance than ELM. In the experiment, our method is compared with the methods based on ELM, such as kernel-ELM, Constrained-ELM and V-ELM, and some state-of–the-art methods on the same dataset. The experimental results show that ML-ELM is much more suitable for motor imagery EEG data and has better performance than the others.

关键词： Electroencephalogram Classification Motor Imagery Extreme Learning Machine Multilayer Extreme Learning Machine

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STABILITY IN p-TH MOMENT FOR UNCERTAIN SINGULAR SYSTEMS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2023年第3期85卷 37-48页

作者： Jia, Zhifu Li, Cunlin School of Sciences and Arts Suqian University Suqian223800 China Ningxia Key Laboratory of intelligent information and Big Data Processing Governance and Social Management Research Center of Northwest Ethnic Regions North Minzu University Yinchuan China

The uncertain singular system is a type of singular system that is disrupted by the canonical Liu process and is described as a multidimensional uncertain differential equation. Although stability in mean, stability in measure, and almost sure stability have been investigated for uncertain singular systems, these three types of stability may not apply in all cases. This paper aims to introduce the concept of stability in p-th moment for uncertain singular systems as a supplementary type of stability. A stability theorem is also presented for uncertain singular systems that are stable in p-th moment. Furthermore, this paper discusses the relationships between stability in measure and stability in p-th moment, between stability in p1-th moment and stability in p2-th moment for uncertain singular systems. An example is provided to demonstrate the effectiveness of our results. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Differential equations

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STABILITY IN p-TH MOMENT FOR UNCERTAIN NONLINEAR SWITCHED SYSTEMS WITH INFINITE-TIME DOMAIN

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第2期86卷 97-108页

The uncertain nonlinear switched system, characterized by its susceptibility to subjective uncertainties, can be described through uncertain differential equations. While investigations have covered mean stability, measure stability, and almost sure stability in the context of uncertain nonlinear switched systems, these forms of stability may not be suitable for every situation. This paper seeks to present the concept of stability in the p-th moment for nonlinear switched systems, offering it as an additional form of stability. The paper also introduces a stability theorem for uncertain nonlinear switched systems that exhibit stability in the p-th moment. Moreover, it explores the connections between stability in measure and stability in the p-th moment within the framework of uncertain nonlinear switched systems. An example is offered to validate the applicability of our outcomes. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Differential equations

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RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer

arXiv

引用

arXiv 2022年

作者： Chen, Yuqi Zhang, Hanyuan Sun, Weiwei Zheng, Baihua School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China Shanghai Institute of Intelligent Electronics & Systems China School of Computing and Information Systems Singapore Management University Singapore

GPS trajectories are the essential foundations for many trajectory-based applications, such as travel time estimation, traffic prediction and trajectory similarity measurement. Most applications require a large number of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means to increase the sample rate of low sample trajectories. Currently, most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of road network and only use grid information or raw GPS points as input. Therefore, the encoder model is not able to capture rich spatial information of the GPS points along the trajectory, making the prediction less accurate and lower spatial consistent. In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It next develops a spatial-temporal transformer model, namely GPSFormer, to learn rich spatial and temporal features along with a Sub-Graph Generation module to capture the spatial features for each GPS point in the trajectory. It finally forwards the outputs of encoder model to a multi-task decoder model to recover the missing GPS points. Extensive experiments based on three large-scale real-life trajectory datasets confirm the effectiveness of our approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Recovery

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Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification

arXiv

引用

arXiv 2024年

作者： Lu, Zhiguang Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing approaches typically develop independent hierarchy-aware models based on shared features extracted from a common base encoder. However, because coarse-grained levels are inherently easier to learn than finer ones, the base encoder tends to prioritize coarse feature abstractions, which impedes the learning of fine-grained features. To overcome this challenge, we propose a novel framework called the Bidirectional Logits Tree (BiLT) for Granularity Reconcilement. The key idea is to develop classifiers sequentially from the finest to the coarsest granularities, rather than parallelly constructing a set of classifiers based on the same input features. In this setup, the outputs of finer-grained classifiers serve as inputs for coarser-grained ones, facilitating the flow of hierarchical semantic information across different granularities. On top of this, we further introduce an Adaptive Intra-Granularity Difference Learning (AIGDL) approach to uncover subtle semantic differences between classes within the same granularity. Extensive experiments demonstrate the effectiveness of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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