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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Opportunities and limitations of explaining quantum machine learning

arXiv

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arXiv 2024年

作者： Gil-Fuster, Elies Naujoks, Jonas R. Montavon, Grégoire Wiegand, Thomas Samek, Wojciech Eisert, Jens Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin14195 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin14109 Germany Department of Electrical Engineering and Computer Science Technische Universität Berlin Berlin14109 Germany Helmholtz-Zentrum Berlin für Materialien und Energie Berlin14109 Germany

A common trait of many machine learning models is that it is often difficult to understand and explain what caused the model to produce the given output. While the explainability of neural networks has been an active field of research in the last years, comparably little is known for quantum machine learning models. Despite a few recent works analyzing some specific aspects of explainability, as of now there is no clear big picture perspective as to what can be expected from quantum learning models in terms of explainability. In this work, we address this issue by identifying promising research avenues in this direction and lining out the expected future results. We additionally propose two explanation methods designed specifically for quantum machine learning models, as first of their kind to the best of our knowledge. Next to our pre-view of the field, we compare both existing and novel methods to explain the predictions of quantum learning models. By studying explainability in quantum machine learning, we can contribute to the sustainable development of the field, preventing trust issues in the future. © 2024, CC BY.

关键词： Adversarial machine learning

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A New Method of Named Entity Recognition on Biology Domain 13

A New Method of Named Entity Recognition on Biology Domain

引用

13th International Conference on Intelligent Computation Technology and Automation, ICICTA 2020

作者： Zhuang, Wande Li, Chengfan Zhang, Qingjie Ding, Xuehai Zhao, Junjuan Shanghai University School of Computer Engineering and Science Shanghai200444 China Shanghai Institute of Advanced Communication and Data Science Shanghai University Shanghai200444 China

ISBN: (纸本)9781728186665

It is the phenotypic data of microbial taxonomy that is of great significance. It can be used not only for the identification of microbial taxonomy but also for the study of the interaction between human and the environment. There are bilstm-CRF, Bert-CRF and other models are commonly used for named entity recognition (NER) recently. However, these methods do not consider the sequence information of neurons and do not analyze the hierarchical structure of sentences. In the field of biological phenotypes, there are longer sentences and complex words, the named entity recognition with neural network performs not very well. Based on this, this paper proposed a new NER method which is named BERT-ON-LSTM to identify and extract microbial phenotypic entities from the literature. This paper improved the Bidirectional Encoder Representations from Transformers (Bert) which enabled the model to learn the hierarchical structure information and be more sensitive to words that require long-term memory. And then, conditional random field (CRF) is used to decode since it can be calculated the joint probability distribution of the whole tag sequence. The experimental results on three data sets prove that our model is effective. © 2020 IEEE.

关键词： Random processes

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Prediction Traffic Flow with Combination Arima and PageRank 3rd

Prediction Traffic Flow with Combination Arima and PageRank

引用

3rd EAI International Conference on Smart Grid and Internet of Things, SGIoT 2019

作者： Li, Cheng-fan Huang, Jia-xin Wu, Shao-chun School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200044 China

ISBN: (纸本)9783030496098

Modern traffic network information is similar to the complex network structure in that the links between the sections are quite complex. Therefore, predicting the traffic flow between sections can effectively relieve traffic congestion. To solve this problem, this paper proposes a combined model of Arima and PageRank to predict the traffic flow of each section of the road network. First, the trained Armia model is used to predict the average speed and traffic flow of each section, and then the PageRank model is used to calculate the weight of each section. The product of traffic flow and weight is output as the final result. Through the experiment of highway traffic data in PeMS database, this method is verified to be able to predict the traffic flow of the whole road network. © 2020, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Traffic congestion

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The Silent Majority: Demystifying Memorization Effect in the Presence of Spurious Correlations

arXiv

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arXiv 2025年

作者： You, Chenyu Dai, Haocheng Min, Yifei Sekhon, Jasjeet S. Joshi, Sarang Duncan, James S. Department of Electrical & Computer Engineering Yale University New HavenCT United States Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT United States Department of Statistics and Data Science Yale University New HavenCT United States Department of Biomedical Engineering Yale University New HavenCT United States Department of Applied Mathematics & Statistics Stony Brook University Stony BrookNY United States Department of Computer Science Stony Brook University Stony BrookNY United States

Machine learning models often rely on simple spurious features - patterns in training data that correlate with targets but are not causally related to them, like image backgrounds in foreground classification. This reliance typically leads to imbalanced test performance across minority and majority groups. In this work, we take a closer look at the fundamental cause of such imbalanced performance through the lens of memorization, which refers to the ability to predict accurately on atypical examples (minority groups) in the training set but failing in achieving the same accuracy in the testing set. This paper systematically shows the ubiquitous existence of spurious features in a small set of neurons within the network, providing the first-ever evidence that memorization may contribute to imbalanced group performance. Through three experimental sources of converging empirical evidence, we find the property of a small subset of neurons or channels in memorizing minority group information. Inspired by these findings, we articulate the hypothesis: the imbalanced group performance is a byproduct of "noisy" spurious memorization confined to a small set of neurons. To further substantiate this hypothesis, we show that eliminating these unnecessary spurious memorization patterns via a novel framework during training can significantly affect the model performance on minority groups. Our experimental results across various architectures and benchmarks offer new insights on how neural networks encode core and spurious knowledge, laying the groundwork for future research in demystifying robustness to spurious correlation. © 2025, CC BY.

关键词： Neurons

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CovidXDetector: Deep learning based chest abnormality detection for covid radiography diagnosis

引用

AIP Conference Proceedings 2024年第1期2853卷

作者： Arun Pandian Rathinakumar N. Yuvaraj D. Premkumar Anjana George Joseph A. Feroz Khan K. R. Sri Preethaa Undergraduate Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India

COVID-19 is a highly infectious disease caused by the coronavirus and has resulted in a global pandemic since 2019. The virus evolves, mutating into several very subtle variants to escape the conventional infection testing methods such as RT-PCR, leaving them obsolete against such new variants. Due to a shortage of resources and rapidly growing infection and mortality rate, a much more robust and more efficient way of testing and diagnosing COVID-19 was developed using deep learning algorithm-based solutions that analyze Chest Radiographs. Existing algorithms suffer from model bias and lack knowledge interpretability by classifying the given image into two classes: healthy or infected. CovidXDetector is a deep learning-based algorithm that detects and localizes Chest Abnormalities on Chest X-Ray Radiographs with critical feature annotations in the image that serves as clinical evidence to diagnose the diagnosis COVID-19. Based on Cascade R-CNN, it achieves an Average Precision score of 0.642 and outperforms YOLOv5 and SSD based detection algorithms by over 14% and 22%, respectively. This study affirms that our proposed model is a practical, easy-to-use and efficient solution for real-time diagnosis of COVID-19 using Chest Radiographs.

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Integrating Blockchain Technology for Secure and Decentralized Network Communication

Integrating Blockchain Technology for Secure and Decentraliz...

引用

Automation and Computation (AUTOCOM), International Conference on

作者： Manish Nagpal Srija Chakraborty Mohit Gupta Raghu N S Vigneshwari Pavan Chaudhary Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Centre for Data Sciences Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electrical and Electronics Engineering JAIN (Deemed to be University) Bangalore Karnataka Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

It is a distributed and decentralized database that protects transparent transaction records. While this technology has been mainly related to crypto currencies, it has a potential application in improving network communication. For instance, the centralized nature of communication can make room for vulnerabilities, including data leakage and single points of failure in a traditional client-server model. You will not have a single point of failure in network effects as communication can be decentralized with block chain. Block chain technology is distributed by nature. Hence, the data is shared and stored on multiple nodes, making it almost impossible for an attacker to modify it. Lastly, Block chain technology offers encryption and authentication for safe device communication without intermediaries. This mitigates the chance of man-in-the-middle attacks and thus helps with data confidentiality and integrity. Besides, using smart contracts in block chain allows attendees to communicate as much as possible without human interference. The introduction of block chain technology in network communication can provide a secure and decentralized solution between client nodes and servers to maintain data privacy and integrity.

关键词： Industries data privacy Smart contracts Interference Medical services Blockchains Encryption Security Servers Financial services

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Optimization Study of Renal CT Image Classification Using Convolution Neural Networks

Optimization Study of Renal CT Image Classification Using Co...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Afnaan. K K. V Nagaraja Tripty Singh Bhanu Prakash KN Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Mathematics Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Principal Investigator Research Scientist Clinical Data Analytics and Radiomics Bioinformatic Institute (BII) A*STAR Singapore

computer tomography (CT), also known as computed tomography, is a medical imaging method that generates detailed and precise horizontal or axial images of targeted regions of the body for diagnostic purposes. In this particular study, the focus was on performing image classification of CT scan images of the Kidney. This organ plays an essential role in detoxification, fluid balance, and maintaining electrolyte levels in the human body. The dataset used for the study contained 12,446 images into four labeled classes - Cyst, Tumor, Stone, and Normal. Convolution Neural Network (CNN) model is built for binary (Normal and Tumor) and multi-image Classification to classify the Renal CT images into four classes. CNN uses ReLU as an activation function for classifying these images. The Model is evaluated and compared based on Accuracy and Loss performance metrics by compiling the CNN model with different deep learning TensorFlow Keras optimizers for Version 2.11.0 for Binary Image Classification at 10,20, and 50 epochs. The CNN model is assembled with Adam Optimizer and Follows the Regularized Leader (FTRL) Optimizer. The Testing and training Accuracy and Losses achieved are evaluated with a learning rate of 0.001 and 0.0001. Adam Optimizer outperforms to be the best Optimizer for Binary and Multi-Image Classification.

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Malware Identification Method Based on Image Analysis

Malware Identification Method Based on Image Analysis

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Yanhua Liu Jiaqi Li Baoxu Liu Xiaoling Gao Ximeng Liu College of Computer and Data Science Fuzhou University Fuzhou China Institute of Information Engineering Beijing China

ISBN: (纸本)9781728194202

In this paper, we propose a malware identification method employed by image analysis and generative adversarial networks, designed to solve the problems of increasingly sophisticated attack forms, insufficient sample data in malware. Specifically, we first generate fixed-size gray images of malware, which neither disassembly nor code execution is required for identification. Moreover, we introduce generative adversarial networks into malware identification for few samples scenarios and malware variants. Through the game training of generator and discriminator, the malware detection model is obtained from the discriminator and the samples are generated by the generator for data augment. Finally, we demonstrate that the proposed method is efficient and feasible using extensive experiments.

关键词： Training Image analysis Codes Games Generative adversarial networks Malware Generators

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Visualization of kernel function of convolutional neural network

Visualization of kernel function of convolutional neural net...

引用

2020 International Conference on Applied Physics and Computing, ICAPC 2020

作者： Li, Chengfan Hu, Zirong Ding, Xuehai School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute of Advanced Communication and Data Science Shanghai University Shanghai200444 China

In recent years, deep learning algorithms have been applied in various fields, and with the continuous development of neural networks, the newly emerging network structures have become more and more complex. However, neural networks are often similar to a black box. The evolution of parameters and the changes of neurons are unknowable. How to understand neural networks is an important research topic. Therefore, for beginners and researchers, a correct way to understand and explain neural networks is needed to improve neural networks. Visualization methods have always been used in the interpretability research of knowledge, which is very helpful to show complex algorithms and networks in the form of specific images, so that users and researchers have significant help in studying the structure of the algorithm and optimizing the performance of the algorithm. The visualization method has been applied to many mining fields. The maximization activation function was been used to realize the visualization of the kernel function in the convolutional neural network (CNN) in this work, and then the network structure and other content are visualized. © 2020 institute of Physics Publishing. All rights reserved.

关键词： Visualization

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