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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Xu Jiayi Yu Jiaqi Wang Yixin Han Shijia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Cross-domain object detection poses significant challenges due to the susceptibility of object detection models to data variance, particularly the domain shifts that can occur between different domains. To address the limitation, we draw inspiration from knowledge distillation, proposing a collaborative learning framework. Our method employs CycleGAN to generate target-style images, and during pretraining, an unsupervised domain adaptation teacher model is trained for each source-target pair. In the distillation process, our proposed algorithm implements an out-of-distribution estimation strategy to select samples that best align with the current model, thereby enhancing the cross-domain distillation process. Furthermore, each expert model is encouraged to collaborate by designating the student model as a bridge between different target domains, facilitated by the Exponential Moving Average (EMA) algorithm. Experiments show that the proposed method leverages structured information, not only does it perform well across various target domains, but it also yields favorable results compared to state-of-the-art unsupervised methods that are specifically trained on single source-target pair.

关键词： Bridges Adaptation models Federated learning Large language models Estimation Object detection Information processing Media Explosions Periodic structures

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Deep Multi-View Clustering via View-Specific Representation and Global Graph

Deep Multi-View Clustering via View-Specific Representation ...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Jingyu Pu Chenhang Cui Yazhou Ren Zichen Wen Tianyi Wu Xiaorong Pu Lifang He School of Computer Science and Engineering University of Electronic Science and Technology of China School of Computer Science and Engineering Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Department of Computer Science and Engineering Lehigh University Bethlehem USA

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

Multi-view clustering can improve clustering performance by leveraging the complementary information from multiview data and has garnered growing interest. Despite significant advancements has been established in this field, most existing methods primarily concentrate on learning the latent representation, neglecting the latent graph that typically reveals relationships between different instances. To address this limitation, this paper presents a new framework of Deep Multi-View Clustering via View-specific Representation and Global Graph (DVGMVC) to address the aforementioned issue. Our method leverages the latent graph of each view to enhance representation quality and incorporates a global graph that combines discriminative information to enhance the performance of deep embedded clustering. Specifically, we employ autoencoders to learn embedded features and construct latent graphs from each view. Subsequently, a weighted two-layer plain residual network refines the latent feature using topological information derived from the latent graphs. As various views adhere to the same foundational cluster structure across multiple views, DVGMVC merges the embedded features of all views to create a unified feature. By leveraging the Graph Convolutional Network (GCN) module, which exploits information from both the latent graphs and the global graph generated from the global feature, a unified embedded feature is generated to capture comprehensive topological information among samples. This unified embedded feature is then employed to generate pseudo labels in a self-supervised manner, guiding unified embedding learning and clustering. In a self-supervised manner, such pseudo-label information guides unified embedding learning and clustering. In-depth experiments on public datasets has proven the state-of-the-art clustering effectiveness of our proposed method.

关键词： Clustering methods Learning (artificial intelligence) Task analysis Residual neural networks

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Towards an Internet-like Power Grid

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 1-11页

作者： Iasonas Kouveliotis-Lysikatos Nikos Hatziargyriou Yanli Liu Felix Wu Department of Electrical Engineering and Computer Science KTH-Royal Institute of TechnologyTeknikringen 3310040StockholmSweden School of Electrical and Computer Engineering National Technical University of Athens15773 AthensGreece School of Electrical and Information Engineering Tianjin UniversityTianjin300072China Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720USA

The great challenges faced by modern power systems require a fresh look at the conventional operation paradigm. The significant challenges faced by modern power systems require an innovative method for the conventional operation paradigm. We claim that the decarbonization of the power grid and extensive electrification of numerous sectors of human activity can only be fostered by a self-adaptable and smart power grid that manifests similar qualities to those of the Internet. The Internet is constructed on a layered architecture that facilitates technology innovations and its intelligence is distributed throughout a hierarchy of networks. In this paper, the fundamental differences between the network data flows and power flows are examined, and the basic requirements for an innovative operation paradigm are highlighted. The current power grid is operated in a highly inflexible, centralized manner to meet increased security goals. A new highly flexible, distributed architecture can be realized by distributing the operation responsibility in smaller areas or even in grid components that can make autonomous decisions. The characteristics of such a power grid are presented, and the key features and advances for the on-going transition to a sustainable power system are identified. Finally, a case study on distributed voltage control is presented and discussed.

关键词： Smart grid Internet distributed intelligence decentralized architecture

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Privacy-preserving data aggregation achieving completeness of data queries in smart grid

Privacy-preserving data aggregation achieving completeness o...

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作者： Li, Xinyang Zhao, Meng Ding, Yong Yang, Changsong Wang, Huiyong Liang, Hai Wang, Yujue Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute of Beihang University Hangzhou China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

In smart grid systems, the control center formulates strategies and provides services by analyzing electricity consumption data. However, ensuring the privacy and security of user data is a critical concern. While traditional data aggregation schemes can provide a certain level of privacy protection for users, they also impose limitations on the control center's access to fine-grained data. To address these challenges, we propose a privacy-preserving data aggregation scheme supporting data query (PAQ). We designed a multi-level data aggregation mechanism based on Paillier semi-homomorphic encryption to achieve efficient aggregation of user data in the control center. Additionally, a data query mechanism based on electricity consumption intervals is introduced, allowing the control center to query aggregated ciphertexts for different user categories from outsourced data on the cloud server. Security analysis demonstrates that PAQ design effectively solves security issues in data aggregation and query processes. Performance analysis indicates that the proposed scheme outperforms existing solutions in terms of efficiency. © 2024 John Wiley & Sons, Ltd.

关键词： Smart power grids

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Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graph

arXiv

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arXiv 2025年

作者： Zhou, Wang-Tao Kang, Zhao Liu, Sicong Zhang, Lizong Tian, Ling School of Computer Science and Engineering University of Electronic Science and Technology of China China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China Kashi Institute of Electronics and Information Industry

Event prediction tasks often handle spatio-temporal data distributed in a large spatial area. Different regions in the area exhibit different characteristics while having latent correlations. This spatial heterogeneity and correlations greatly affect the spatio-temporal distributions of event occurrences, which has not been addressed by state-of-the-art models. Learning spatial dependencies of events in a continuous space is challenging due to its fine granularity and a lack of prior knowledge. In this work, we propose a novel Graph Spatio-Temporal Point Process (GSTPP) model for fine-grained event prediction. It adopts an encoder-decoder architecture that jointly models the state dynamics of spatially localized regions using neural Ordinary Differential Equations (ODEs). The state evolution is built on the foundation of a novel Self-Adaptive Anchor Graph (SAAG) that captures spatial dependencies. By adaptively localizing the anchor nodes in the space and jointly constructing the correlation edges between them, the SAAG enhances the model's ability of learning complex spatial event patterns. The proposed GSTPP model greatly improves the accuracy of fine-grained event prediction. Extensive experimental results show that our method greatly improves the prediction accuracy over existing spatio-temporal event prediction approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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Temporal-guided Mixture-of-Experts for Zero-Shot Video Question Answering

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Qin, Yixin Zhao, Lei Gao, Lianli Zhang, Haonan Zeng, Pengpeng Shen, Heng Tao University of Electronic Science and Technology of China Center for Future Media School of Computer Science and Engineering Chengdu611731 China Sichuan Artificial Intelligence Research Institute Yibin644000 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China Tongji University Department of Information Engineering and Computer Science China

Video Question Answering (VideoQA) is a challenging task in the vision-language field. Due to the time-consuming and labor-intensive labeling process of the question-answer pairs, fully supervised methods are no longer suitable for the current increasing demand for data. This has led to the rise of zero-shot VideoQA, and some works propose to adapt large language models (LLMs) to assist zero-shot learning. Despite recent progress, the inadequacy of LLMs in comprehending temporal information in videos and the neglect of temporal differences, e.g., the different dynamic changes between scenes or objects, remain insufficiently addressed by existing attempts in zero-shot VideoQA. In light of these challenges, a novel Temporal-guided Mixture-of-Experts Network (T-MoENet) for zero-shot video question answering is proposed in this paper. Specifically, we apply a temporal module to imbue language models with the capacity to perceive temporal information. Then a temporal-guided mixture-of-experts module is proposed to further learn the temporal differences presented in different videos. It enables the model to effectively improve the capacity of generalization. Our proposed method achieves state-of-the-art performance on multiple zero-shot VideoQA benchmarks, notably improving accuracy by 5.6% on TGIF-FrameQA and 2.3% on MSRVTT-QA while remaining competitive with other methods in the fully supervised setting. The codes and models developed in this study will be made publicly available at https://***/qyx1121/T-MoENet. © 1991-2012 IEEE.

关键词： Zero-shot learning

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Digital Twin data Management: A Comprehensive Review

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IEEE Transactions on Big data 2025年

作者： Ouedraogo, Ezekiel B. Hawbani, Ammar Wang, Xingfu Liu, Zhi Zhao, Liang Al-Qaness, Mohammed A. A. Alsamhi, Saeed Hamood University of Science and Technology of China School of Computer Science and Technology Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China The University of Electro-Communications Tokyo Japan Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China University of Galway Insight centre for data analytics Ireland IBB University Ibb Faculty of Engineering Yemen Yemen

Digital Twins are virtual representations of physical assets and systems that rely on effective data Management to integrate, process, and analyze diverse data sources. This article comprehensively examines data Management challenges, architectures, techniques, and applications in the context of Digital Twins. It explores key issues such as data heterogeneity, quality assurance, scalability, security, and interoperability. The paper outlines architectural approaches like centralized, distributed, cloud-based, and blockchain solutions and data Management techniques for modeling, integration, fusion, quality management, and visualization. Domain-specific considerations across manufacturing, smart cities, healthcare, and other sectors are discussed. Finally, open research challenges related to standards, real-time data processing, intelligent data Management, and ethical aspects are highlighted. By synthesizing the state-of-the-art, this review serves as a valuable reference for developing robust data Management strategies that enable Digital Twin deployments. © 2015 IEEE.

关键词： data quality

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QUALYST: data Quality Assessment System for Thailand Open Government data

QUALYST: Data Quality Assessment System for Thailand Open Go...

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International Joint Conference on computer science and Software engineering (JCSSE)

作者： Tanapat Samakit Chutiporn Anutariya Marut Buranarach ICT Department Computer Science & Information Management School of Engineering and Technology Asian Institute of Technology Thailand Data Science and Analytics Research Group National Electronics and Computer Technology Center Thailand

Open Government data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all; thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format.

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Fuzzy Multivariate Variational Mode Decomposition With Applications in EEG Analysis

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1937-1948页

作者： Tang, Hongkai Yang, Xun Yuan, Yixuan Vidal, Pierre-Paul Wang, Danping Cao, Jiuwen Wu, Duanpo Hangzhou Dianzi University School of Computer Science Hangzhou310018 China University of Science and Technology of China Department of Electronic Engineering and Information Science School of Information Science and Technology Hefei230026 China The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hangzhou Dianzi University Machine Learning and I-health International Cooperation Base of Zhejiang Province Hangzhou310018 China Université Paris Saclay ENS Paris Saclay Centre Borelli CNRS SSA INSERM Université Paris Cité Paris75006 France Université de Paris INSERM US36 CNRS UAR2009 Plateforme d'Etude de la Sensorimotricité Paris75006 France BioMedTech Facilities Paris75270 France Hangzhou Dianzi University Artificial Intelligence Institute Hangzhou310018 China Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China

This article introduces a novel extension of the multivariate variational mode decomposition (MVMD) method, termed fuzzy MVMD (FMVMD), designed to enhance alignment information extraction. In contrast to MVMD, FMVMD focuses on capturing finer alignment details by leveraging fuzzy clustering techniques. The proposed FMVMD algorithm proceeds through the following steps: First, FMVMD employs a modified clustering algorithm, termed fuzzy C-means (FCM), to categorize submodes within each channel into fuzzy clusters based on their contribution to common center frequencies. Second, a variational optimization model is formulated, extending the principles of MVMD to accommodate the fuzzy clustering approach used in FMVMD. Finally, an optimization technique called the alternating direction method of multipliers is employed to derive the optimal solution for the FMVMD model. Experimental results show that FMVMD achieves a 41% and 28% improvement in center frequency alignment performance compared to MVMD when using two and three fuzzy clusters, respectively, and a 13% improvement compared to GMVMD with the same number of clusters. Under a 25 dB SNR condition, FMVMD demonstrates a noise resistance improvement of 44% and 24% compared to MVMD with two and three fuzzy clusters, respectively, and a 37% improvement compared to GMVMD. Validation using EEG data in the forms of bipolar leads and common average reference confirms the effectiveness of FMVMD, achieving consistently favorable results. © 1993-2012 IEEE.

关键词： Variational mode decomposition

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Diverse Embedding Modeling with Adaptive Noise Filter for Text-based Person Retrieval

Diverse Embedding Modeling with Adaptive Noise Filter for Te...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chen He Shenshen Li Zheng Wang Fumin Shen Yang Yang Xing Xu School of Computer Science and Engineering University of Electronic Science and Technology of China China College of Electronic and Information Engineering Tongji University China Institute of Electronic and Information Engineering of UESTC in Guangdong China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Text-based person retrieval (TBPR) involves retrieving pedestrian images from a gallery using textual queries. Existing methods assume that all the training pairs are correct and the textual query only corresponds to one image. However, in practical scenarios of TBPR, there indeed exists data noise and many-to-many matching relationships between semantically similar images and textual queries. To address these problems, we propose a novel approach termed Diverse Embedding Modeling (DEM) with Adaptive Noise Filter for the TBPR task. Firstly, we propose a dynamic margin to measure the degree of noise, which can adaptively reduce the weights of image-text pairs with severe noise during training, thereby effectively mitigating the impact of noisy pairs. Moreover, we model diverse visual and textual embeddings from learnable parameterized distributions, which aim to simulate the many-to-many matching scenarios. Extensive experiments conducted on three TBPR datasets demonstrate the superior performance of our DEM method compared to recent state-of-the-art methods.

关键词： Training Weight measurement Matched filters Adaptation models Visualization Pedestrians Noise Adaptive filters Generators Noise measurement

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