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arXiv 2024年

作者： Nguyen, Nhan Duc Thanh Phan, Huy Geirnaert, Simon Mikkelsen, Kaare Kidmose, Preben Center for Ear-EEG Department of Electrical and Computer Engineering Aarhus University Aarhus N8200 Denmark Amazon CambridgeMA02142 United States Stadius Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven and Leuven.AI - KU Leuven institute for AI Kasteelpark Arenberg 10 LeuvenB-3001 Belgium The Department of Neurosciences Research Group ExpORL Herestraat 49 box 721 LeuvenB-3000 Belgium

Auditory attention decoding (AAD) is the process of identifying the attended speech in a multi-talker environment using brain signals, typically recorded through electroencephalography (EEG). Over the past decade, AAD has undergone continuous development, driven by its promising application in neuro-steered hearing devices. Most AAD algorithms are relying on the increase in neural entrainment to the envelope of attended speech, as compared to unattended speech, typically using a two-step approach. First, the algorithm predicts representations of the attended speech signal envelopes;second, it identifies the attended speech by finding the highest correlation between the predictions and the representations of the actual speech signals. In this study, we proposed a novel end-to-end neural network architecture, named AADNet, which combines these two stages into a direct approach to address the AAD problem. We compare the proposed network against the traditional approaches, including linear stimulus reconstruction, canonical correlation analysis, and an alternative non-linear stimulus reconstruction using two different datasets. AADNet shows a significant performance improvement for both subject-specific and subject-independent models. Notably, the average subject-independent classification accuracies from 56.1% to 82.7% with analysis window lengths ranging from 1 to 40 seconds, respectively, show a significantly improved ability to generalize to data from unseen subjects. These results highlight the potential of deep learning models for advancing AAD, with promising implications for future hearing aids, assistive devices, and clinical assessments. © 2024, CC BY-NC-SA.

关键词： Hearing aids

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Enhancing Subject-Independent EEG-Based Auditory Attention Decoding with WGAN and Pearson Correlation Coefficient

Enhancing Subject-Independent EEG-Based Auditory Attention D...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Saurav Pahuja Gabriel Ivucic Felix Putze Siqi Cai Haizhou Li Tanja Schultz Machine Listening Lab (MLL) University of Bremen Germany Cognitive Systems Lab (CSL) University of Bremen Germany Department of Electrical and Computer Engineering National University of Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Kriston AI Xiamen China CSL and hosts MLL

Electroencephalography (EEG) related research faces a significant challenge of subject independence due to the variation in brain signals and responses among individuals. While deep learning models hold promise in addressing this challenge, their effectiveness depends on large datasets for training and generalization across participants. To overcome this limitation, we propose a solution to the above limitation by increasing the size and quality of training data for subject-independent auditory attention decoding (AAD) using EEG with deep learning. Specifically, our method employs a Wasserstein Generative Adversarial Network (WGAN) to generate synthetic data, with Pearson correlation filtering the most realistic samples. We evaluated this method on a publicly available dataset of selective auditory attention experiments and showed superior performance in subject-independent AAD performance. The mixed training set, consisting of both real and artificial data generated by the WGAN+Pearson Correlation Coefficient, demonstrated approximately 4% improvement in AAD accuracy for a 1-second window. These results demonstrate that deep learning remains a viable approach to overcoming data scarcity in subject-independent AAD tasks based on EEG. Moreover, the proposed method has the potential to improve the generalization and reliability of EEG classification tasks.

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Machine Learning Approaches for Advanced Threat Detection in Cyber Security

Machine Learning Approaches for Advanced Threat Detection in...

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Global Conference for Advancement in Technology (GCAT)

作者： V. Saravanan Talluri Upender E. D. Kanmani Ruby P. Deepalakshmi Desidi Narsimha Reddy Ananthi S N Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of CSE CMR College of Engineering and Technology. Medchal Hyderabad Telangana Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science Technology School of Computing Director - Accreditation and Ranking Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of Computer Science and Engineering S.A. Engineering College Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The dynamic nature of cyber threats demands that threat detection mechanisms be continuously improved in order to protect digital assets and infrastructures. In cybersecurity, this study investigates the use of machine learning (ML) algorithms for enhanced threat detection. This work intends to improve threat detection systems' accuracy, speed, and adaptability by utilizing ML algorithms' capabilities. The abstract begins by outlining the significance of machine learning in transforming traditional cybersecurity measures. It highlights the limitations of conventional signature-based methods, which often fail to detect novel and sophisticated threats. In contrast, ML-based approaches offer dynamic and proactive defenses capable of identifying previously unseen attack vectors. Furthermore, the paper delves into the practical implementation of ML models in real-world cybersecurity environments. It covers data collection and preprocessing techniques critical for training accurate and reliable models, as well as the challenges associated with maintaining and updating these models to adapt to evolving threat landscapes. The importance of feature selection and engineering in improving model performance is also emphasized. To validate the effectiveness of ML approaches, the study presents case studies and experimental results from various cybersecurity applications. The abstract concludes by addressing the future prospects and potential advancements in machine learning for cybersecurity. It discusses the role of emerging technologies, such as deep learning and federated learning, in enhancing threat detection capabilities. Additionally, it considers the ethical and privacy concerns associated with deploying ML models in cybersecurity and the need for robust governance frameworks. In summary, this study presents a thorough analysis of machine learning techniques for enhanced threat detection in cybersecurity, including useful applications, advantages, and difficulties.

关键词： Adaptation models Machine learning algorithms Accuracy Heuristic algorithms Machine learning data collection Threat assessment data models Vectors computer security

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The SVD of Convolutional Weights: A CNN Interpretability Framework

arXiv

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arXiv 2022年

作者： Praggastis, Brenda Brown, Davis Marrero, Carlos Ortiz Purvine, Emilie Shapiro, Madelyn Wang, Bei AI & Data Analytics Division Pacific Northwest National Laboratory SeattleWA98109 United States AI & Data Analytics Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States Institute University of Utah Salt Lake CityUT94112 United States

Deep neural networks used for image classification often use convolutional filters to extract distinguishing features before passing them to a linear classifier. Most interpretability literature focuses on providing semantic meaning to convolutional filters to explain a model’s reasoning process and confirm its use of relevant information from the input domain. Fully connected layers can be studied by decomposing their weight matrices using a singular value decomposition, in effect studying the correlations between the rows in each matrix to discover the dynamics of the map. In this work we define a singular value decomposition for the weight tensor of a convolutional layer, which provides an analogous understanding of the correlations between filters, exposing the dynamics of the convolutional map. We validate our definition using recent results in random matrix theory. By applying the decomposition across the linear layers of an image classification network we suggest a framework against which interpretability methods might be applied using hypergraphs to model class separation. Rather than looking to the activations to explain the network, we use the singular vectors with the greatest corresponding singular values for each linear layer to identify those features most important to the network. We illustrate our approach with examples and introduce the DeepdataProfiler library, the analysis tool used for this *** Codes 68T07, 68T01, 05C65 © 2022, CC BY.

关键词： Singular value decomposition

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Generalized Category Discovery with Decoupled Prototypical Network

arXiv

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arXiv 2022年

作者： An, Wenbin Tian, Feng Zheng, Qinghua Ding, Wei Wang, QianYing Chen, Ping School of Automation Science and Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Laboratory for Big Data Analytics China Department of Computer Science University of Massachusetts Boston United States Lenovo Research Department of Engineering University of Massachusetts Boston United States

Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Without considering differences between known and novel categories, current methods learn about them in a coupled manner, which can hurt model’s generalization and discriminative ability. Furthermore, the coupled training approach prevents these models transferring category-specific knowledge explicitly from labeled data to unlabeled data, which can lose high-level semantic information and impair model performance. To mitigate above limitations, we present a novel model called Decoupled Prototypical Network (DPN). By formulating a bipartite matching problem for category prototypes, DPN can not only decouple known and novel categories to achieve different training targets effectively, but also align known categories in labeled and unlabeled data to transfer category-specific knowledge explicitly and capture high-level semantics. Furthermore, DPN can learn more discriminative features for both known and novel categories through our proposed Semantic-aware Prototypical Learning (SPL). Besides capturing meaningful semantic information, SPL can also alleviate the noise of hard pseudo labels through semantic-weighted soft assignment. Extensive experiments show that DPN outperforms state-of-the-art models by a large margin on all evaluation metrics across multiple benchmark datasets. Code and data are available at https://***/Lackel/DPN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Breast lesion detection from DCE-MRI using YOLOv7

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aiP Conference Proceedings 2024年第1期3030卷

作者： Bahadir Kulavuz Mustafa Cavusoglu Bulent Bayram Tolga Bakirman Sinan Sahin Nusret Araz Gozde Orhan Hulusi Emre Surmeli Tuna Cakar Yildiz Technical University Faculty of Civil Engineering Department of Geomatics Engineering Istanbul Turkiye MLPCARE Data Science and AI Department istanbul Turkiye Graduate School of Big Data Analytics MEF University Istanbul Turkiye

Breast cancer is one of the most common types of cancer among women. Early diagnosis of breast cancer has vital importance to prevent unexpected losses. A worldwide effort has been made to tackle early detection challenge. Dynamic contrast-enhanced magnetic resonance imaging is a superior imaging system that improves breast cancer diagnosis quality of physicians. computer aided Diagnosis systems are used as a complementary tool to improve breast cancer diagnosis. In last decades, various computer aided diagnosis systems have been proposed. However, the state-of-the-art deep learning-based approaches have started to overcome conventional medical image processing methods. In this study, we aimed to detect malignant breast lesions from open access dynamic contrast-enhanced magnetic resonance imagery dataset using most recent YOLOv7 deep learning architecture. 2400 images have been used for training (80%) and testing (20%) of the network. The metrics calculated with the test dataset are 98.54%, 96.42% and 84.40% for mAP@0.50 IoU, mAP@0.75 IoU and mAP, respectively. The results show that YOLOv7 architecture is capable to detect malignant breast lesions from dynamic contrast-enhanced magnetic resonance images efficiently.

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FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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Optimized Security Mechanism for publicly Secret Key Sharing over Cloud using Blockchain

Optimized Security Mechanism for publicly Secret Key Sharing...

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2024 International Symposium on Securing Next Generation Systems Using Future Artificial Intelligence Technologies, SNSFaiT 2024

作者： Byeon, Haewon Quraishi, Aadam Thuniki, Praveen Keshta, Ismail Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Intervention Treatment Institute HoustonTX United States Sr Program Analyst Georgia CummingGA30040 United States Computer Science and Information Systems Department College of Applied Sciences Al Maarefa University Riyadh Saudi Arabia Dr. D. Y. Patii Vidyapeeth Pune Dr. D. Y. Patii School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving it, effectively preventing it from being abused. The crucial sections of the nodes that participated in the critical splicing are made public during the critical recovery stage to prevent them from performing harmful things during the critical recovery stage. Add IDs to the nodes that participated in the crucial splicing during the key distribution stage;a dynamic threshold system is intended to track and update the status of malicious nodes in real time. When the node that possesses the crucial component fails, the owner of the critical component and the main node relocate a key element to the new participating nodes. To safeguard sensitive information. The experimental results show that this system has a key recovery rate of 80% and threshold qualities such as traceability, enforceability, and recoverability. © 2024 Copyright for this paper by its authors.

关键词： Blockchain Encryption Key sharing mechanism Privacy Secret key Security

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Towards Seamless Connectivity: Implementing 6G Communication Technologies In Next-Generation Networks

Towards Seamless Connectivity: Implementing 6G Communication...

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Advancement in Technology (ICONAT), International Conference for

作者： K. Kalaiselvi R. Sankar S Supriya G Kaushik Hemanth Swamy M Devika Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamilnadu India Department of Electrical and Electronics Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Senior Software Engineer Motorola Solutions Department of AI& DS Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). Despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can’t meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, ai-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues.

关键词： 6G mobile communication 5G mobile communication Wireless networks Complex networks Market research Communications technology Internet of Things Optimization Next generation networking Edge computing

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Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation

arXiv

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arXiv 2024年

作者： Li, Jiyong Azizov, Dilshod Li, Yang Liang, Shangsong School of Computer Science and Engineering Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates AI Thrust Information Hub The Hong Kong University of Science and Technology Guangzhou China Department of CSE The Hong Kong University of Science and Technology China

Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual Learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions with a new strategy for Replay Buffer Selection (RBS), which minimize estimated variance to save hard negative samples for representation learning with high quality. Furthermore, we present the Prototype-instance Relation Distillation (PRD) loss, a technique designed to maintain the relationship between prototypes and sample representations using a self-distillation process. Experiments on standard continual learning benchmarks reveal that our method notably outperforms existing baselines in terms of knowledge preservation and thereby effectively counteracts catastrophic forgetting in online contexts. The code is available at https://***/lijy373/CCLIS. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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