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arXiv 2024年

作者： Liu, Huan Yang, Shusen Zhang, Yuzhe Wang, Mengze Gong, Fanyu Xie, Chengxi Liu, Guanjian Liu, Zejun Liu, Yong-Jin Lu, Bao-Liang Zhang, Dalin The School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China The School of Software Engineering Xi’an Jiaotong University Xi’an710049 China The Joint School of Design and Innovation Xi’an Jiaotong University Xi’an710049 China The Department of Computer Science and Technology Tsinghua University Beijing100084 China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Space Information Research Institute Hangzhou Dianzi University Hangzhou310018 China

EEG-based emotion recognition (EER) has gained significant attention due to its potential for understanding and analyzing human emotions. While recent advancements in deep learning techniques have substantially improved EER, the field lacks a convincing benchmark and comprehensive open-source libraries. This absence complicates fair comparisons between models and creates reproducibility challenges for practitioners, which collectively hinder progress. To address these issues, we introduce LibEER, a comprehensive benchmark and algorithm library designed to facilitate fair comparisons in EER. LibEER carefully selects popular and powerful baselines, harmonizes key implementation details across methods, and provides a standardized codebase in PyTorch. By offering a consistent evaluation framework with standardized experimental settings, LibEER enables unbiased assessments of over ten representative deep learning models for EER across the four most widely used datasets. Additionally, we conduct a thorough, reproducible comparison of model performance and efficiency, providing valuable insights to guide researchers in the selection and design of EER models. Moreover, we make observations and in-depth analysis on the experiment results and identify current challenges in this community. We hope that our work will not only lower entry barriers for newcomers to EEG-based emotion recognition but also contribute to the standardization of research in this domain, fostering steady development. The library and source code are publicly available at https://***/XJTU-EEG/LibEER. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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MaGIC: Multi-modality Guided Image Completion

arXiv

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arXiv 2023年

作者： Yu, Yongsheng Wang, Hao Luo, Tiejian Fan, Heng Zhang, Libo Department of Computer Science University of Rochester United States Institute of Software Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Department of Computer Science and Engineering University of North Texas United States

Vanilla image completion approaches exhibit sensitivity to large missing regions, attributed to the limited availability of reference information for plausible generation. To mitigate this, existing methods incorporate the extra cue as a guidance for image completion. Despite improvements, these approaches are often restricted to employing a single modality (e.g., segmentation or sketch maps), which lacks scalability in leveraging multi-modality for more plausible completion. In this paper, we propose a novel, simple yet effective method for Multi-modal Guided Image Completion, dubbed MaGIC, which not only supports a wide range of single modality as the guidance (e.g., text, canny edge, sketch, segmentation, depth, and pose), but also adapts to arbitrarily customized combination of these modalities (i.e., arbitrary multi-modality) for image completion. For building MaGIC, we first introduce a modality-specific conditional U-Net (MCU-Net) that injects single-modal signal into a U-Net denoiser for single-modal guided image completion. Then, we devise a consistent modality blending (CMB) method to leverage modality signals encoded in multiple learned MCU-Nets through gradient guidance in latent space. Our CMB is training-free, thereby avoids the cumbersome joint re-training of different modalities, which is the secret of MaGIC to achieve exceptional flexibility in accommodating new modalities for completion. Experiments show the superiority of MaGIC over state-of-the-art methods and its generalization to various completion tasks. Our project with code and models is available at ***/MaGIC-Page/. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Large Capacity Image Watermarking Model using SWT and Laplacian Pyramid

Large Capacity Image Watermarking Model using SWT and Laplac...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Baowei Wang Fan Yang Yufeng Wu Changyu Dai Wenjue Huang Xingyuan Zhao Engineering Research Center of Digital Forensics Ministry of Education China Waterford Institute Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

Image watermarking algorithms have developed rapidly in recent years. Most of the Deep Neural Network(DNN)-based image watermarking algorithms embed only 30 bits or 64 bits messages in 128 × 128 images, while few algorithms embed large capacity messages. This is mainly because embedding large capacity messages will significantly influence the performance of algorithms. However, the limited capacity of these messages inhibits their effectiveness in certain data protection scenarios. Therefore, we propose a DNN-based large capacity image water-marking model. By using Stationary Wavelet Transform(SWT) and Laplacian pyramid transform, we expand the embedding capacity and achieve embedding 128 bits messages in 128 × 128 images. Experimental results show that under the same conditions, the proposed model is superior to state-of-the-art models, and has excellent performance.

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A Topology Independent Fast Rerouting Scheme with Hop-by-Hop Forwarding Can Achieve a Full Protection

SSRN

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SSRN 2022年

作者： Geng, Haijun Meng, Zhuo Zhang, Han Shi, Xingang Wang, Zhiliang Yin, Xia Chen, Meng Guo, Yingya The School of Automation and Software Engineering Shanxi University School of Computer and Information Technology China The Institute for Network Sciences and Cyberspace Tsinghua University China The Department of Computer Science and Technology Tsinghua University China The School of Big Data and Software Engineering Chongqing University China The College of Computer and Data Science Fuzhou University China

To support critical applications or guarantee a high quality of service, Internet Service Providers (ISPs) generally deploy a technique called routing protection algorithm for local fast rerouting. Alternate next hops are stored alongside with the default next hops in a router’s forwarding table, and can be immediately activated to invoke a loop free repair path in face of link or node failures, or used for multipath transmission. However, the existing routing protection methods either cannot protect all possible single-failure scenarios in the network or needs the assistance of an auxiliary address. In response to the above problems, the present study proposes a single-failure routing protection algorithm based on hop by hop routing (SPA). The algorithm has the following three advantages: (1) supports hop-by-hop forwarding, (2) supports incremental deployment, and (3) can protect all possible singlefailure scenarios in the network as long as network keep connected. Extensive simulations show that, compared to the state-of-the-art schemes which are adopted by the industry, SPA not only can handle arbitrary single-failure situations in the network but also has a small path stretch. These advantages make SPA a good candidate for practical solution for IP fast reroute when network failure occurs. © 2022, The Authors. All rights reserved.

关键词： Internet service providers

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Cost Minimization in Serverless Computing with Energy Harvesting SECs

Cost Minimization in Serverless Computing with Energy Harves...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Yunqi Li Jing Liu Bin Jiang Changlin Yang Qingtian Wang School of Computer Science Zhongyuan University of Technology State Information Center Beijing China China Scholarship Council School of Software Engineering Sun Yat-sen University China Telecom Research Institute Beijing China

With an increasing number of Mobile Users (MUs), Multi-access edge computing (MEC) has become a bottleneck in resource limitation. Serverless edge computing (SEC) is a promising approach to effectively alleviate the shortage of MEC. However, existing research on SEC focus on the operating mode of the SEC server, they ignore the interaction between SEC and MU. To this end, we propose a Stackelberg game approach to maximize the utility of each MU. We present the model of the Stackelberg game and propose an iterative algorithm as the solution. We also consider the impact of the function resource pool and using renewable energy on SEC. In particular, when a function that required by a MU is not stored in this SEC, it downloads the function from could with extra cost. Meanwhile, the SEC has a lower cost by using harvested energy rather than purchasing from the grid. Simulation results show that the proposed scheme is efficient in terms of SEC’s profit and MU’s demand. Moreover, both MUs and SECs gain benefits from renewable energy.

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Power Saving Design of Active Reconfigurable Intelligent Surface―A Sub-Array Architecture

Power Saving Design of Active Reconfigurable Intelligent Sur...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yanze Zhu Yang Liu Ming Li Qingqing Wu Qingjiang Shi School of Information and Communication Engineering Dalian University of Technology Department of Electrical and Computer Engineering University of Macau School of Software Engineering Tongji University and Shenzhen Research Institute of Big Data

ISBN: (纸本)9781665435413

Reconfigurable intelligent surface (RIS) is envisioned as a promising technology to enhance future wireless communication systems. Very recently, a novel active RIS architecture has been proposed via introducing amplifiers into the reflecting elements. Although these embedded amplifiers can effectively extend the RIS coverage, they also bring non-negligible energy expenditure. To overcome this drawback, this paper proposes a novel sub-array based structure, which divides the entire RIS array into multiple sub-arrays with each being flexibly turned on/off. We aim to minimize the power consumption of the whole system via jointly activating sub-arrays and designing beamforming, which is highly challenging due to its combinatorial nature. Via inducing the group sparsity and leveraging the majorization-minimization (MM) approach, we develop an efficient solution to resolve this challenge. Numerical results demonstrate that our proposed sub-array structure can significantly reduce the power consumption compared to the conventional “all-on” scheme.

关键词： Wireless communication Power demand Array signal processing Reconfigurable intelligent surfaces Minimization Global communication

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A Blockchain-Based Distributed Collaborative Sensing and Spectrum Access Approach for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Peng, Yuhuai Li, Yuan Guo, Yu Zhang, Dawei Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Northeastern University College of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Xihu District Hangzhou China Zhengzhou University School of Computer and Artificial Intelligence Henan Zhengzhou450001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China KKR and KSR Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India Division of Research and Development Lovely Professional University Phagwara India University Centre of Research and Development Chandigarh University Mohali India Umm Al-Qura University Department of Software Engineering College of Computing Makkah Saudi Arabia Umm Al-Qura University College of Computing Department of Computer and Network Engineering Makkah Saudi Arabia

Cognitive radio (CR) provides key technical support for the seamless communication of consumer electronic (CE) devices through distributed cooperative sensing and spectrum access. However, its openness and dynamic nature introduce potential security risks and challenges. Due to the openness of wireless communication, spectrum sensing and access are vulnerable to malicious attacks, and resource-constrained devices cannot effectively implement artificial intelligence (AI)-driven encryption and access control strategies. To address these limitations, this study proposes a blockchain-based distributed collaborative sensing method for CE devices. In the proposed method, after sensing, the smart contracts verify node legitimacy using the ring signature method. The malicious users are rejected by the outlier detection method, which improves the collaborative anti-interference ability. In addition, by using a centralized training and distributed execution architecture, a secure spectrum sensing strategy based on multi-agent reinforcement learning is designed. The proposed strategy ensures that each node makes independent decisions, which enhances the adaptability of the CE ecosystem. The proposed method is verified by extensive simulations, and the results show that it can maintain spectrum awareness of more than 95.72% and an access success rate of 91.19% under malicious attacks. Moreover, communication overhead is significantly reduced by approximately 93.53%. © 1975-2011 IEEE.

关键词： Blockchain

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Development of a Model for Discriminating Electroencephalograms of Patients with Mild Cognitive Impairment and Healthy Individuals Using a Support Vector Machine

Development of a Model for Discriminating Electroencephalogr...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Akifumi Tanabe Kyosuke Nakatani Kento Harachi Yusuke Yamamoto Ayumi Muramatsu Hajime Nagahara Noriko Takemura Shinji Shimojo Daisuke Furihata Yuko Mizuno-Matsumoto Kazue Kamae-Shigenobu Kazuhiro Shinosaki Graduate School of Information Science University of Hyogo Kobe Japan School of Life Science and Technology Tokyo Institute of Technology Yokohama Japan Department of Medical Engineering Aino University Ibaraki Japan Institute for Datability Science Osaka University Suita Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Software and Information Technology Aomori University Aomori Japan Cyber media center Osaka University Suita Japan Asakayama General Hospital Sakai Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

We performed machine learning using spectrum analysis and power spectra of electroencephalograms (EEGs) of patients with mild cognitive impairment (MCI). Spectrum analysis was performed on the EEGs of MCI patients and healthy individuals, and the power spectrum values of the two groups were calculated and compared. Based on the power spectrum values of the theta and alpha bands, models for discriminating MCI patients were created using a support vector machine (SVM), and their accuracies were compared. In the spectrum analysis, the theta band showed significantly higher power spectrum values in the MCI group. In the comparison of the accuracies of SVM models, the model using the theta band showed higher values in parameters, such as accuracy rate. These results suggested that the power spectrum values of the theta band are useful for discriminating MCI patients.

关键词： Support vector machines Electrodes Analytical models Accuracy Machine learning Brain modeling Electroencephalography Vectors Spectral analysis

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ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization

arXiv

引用

arXiv 2023年

作者： Guo, Jintao Wang, Na Qi, Lei Shi, Yinghuan State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China School of Computer Science and Engineering Southeast University China

Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are based on convolutional neural networks (CNNs). However, the local operation of the convolution kernel makes the model focus too much on local representations (e.g., texture), which inherently causes the model more prone to overfit to the source domains and hampers its generalization ability. Recently, several MLP-based methods have achieved promising results in supervised learning tasks by learning global interactions among different patches of the image. Inspired by this, in this paper, we first analyze the difference between CNN and MLP methods in DG and find that MLP methods exhibit a better generalization ability because they can better capture the global representations (e.g., structure) than CNN methods. Then, based on a recent lightweight MLP method, we obtain a strong baseline that outperforms most state-of-the-art CNN-based methods. The baseline can learn global structure representations with a filter to suppress structure-irrelevant information in the frequency space. Moreover, we propose a dynAmic LOw-Frequency spectrum Transform (ALOFT) that can perturb local texture features while preserving global structure features, thus enabling the filter to remove structure-irrelevant information sufficiently. Extensive experiments on four benchmarks have demonstrated that our method can achieve great performance improvement with a small number of parameters compared to SOTA CNN-based DG methods. Our code is available at https://***/lingeringlight/ALOFT/. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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DomainAdaptor: A Novel Approach to Test-time Adaptation

arXiv

引用

arXiv 2023年

作者： Zhang, Jian Qi, Lei Shi, Yinghuan Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China School of Computer Science and Engineering Southeast University China

To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificity of unseen samples that are also critical during the test. In this paper, we investigate a more challenging task that aims to adapt a trained CNN model to unseen domains during the test. To maximumly mine the information in the test data, we propose a unified method called DomainAdaptor for the test-time adaptation, which consists of an AdaMixBN module and a Generalized Entropy Minimization (GEM) loss. Specifically, AdaMixBN addresses the domain shift by adaptively fusing training and test statistics in the normalization layer via a dynamic mixture coefficient and a statistic transformation operation. To further enhance the adaptation ability of AdaMixBN, we design a GEM loss that extends the Entropy Minimization loss to better exploit the information in the test data. Extensive experiments show that DomainAdaptor consistently outperforms the state-of-the-art methods on four benchmarks. Furthermore, our method brings more remarkable improvement against existing methods on the few-data unseen domain. The code is available at https://***/koncle/DomainAdaptor. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

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