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Physical Layer Authentication via Underwater Acoustic Multipath Channel Crafting

Physical Layer Authentication via Underwater Acoustic Multip...

引用

OCEANS

作者： Davide Eccher Paolo Casari Dept. of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

With the increasing number of actors in the under-water environment and the development of new applications, such as large-scale monitoring and autonomous underwater vehicle control, securing underwater communications is becoming a primary necessity. Security was not prioritized in the past due to the constraints of underwater acoustic communications, which cannot sustain the overhead of typical cryptographic techniques. In this paper, we propose a method to authenticate a network device by exploiting the physical properties of the acoustic channel. In particular, our method hinges on the uniqueness and quasi-reciprocity of the channel, from which the authenticator (Alice) node can extract several parameters such as the number of multi path channel components, their delay and amplitude. These values are similar on both ends of a link between Alice and a legitimate transmitter (Bob), and can be used as a seed to craft a new artificial channel, that is then applied to transmissions from Bob to Alice. With this procedure, Alice can distinguish Bob from an impersonating attacker (Eve), given a previous message exchange history. Eve can try to bypass the protocol by estimating the channel parameters and by trying to replicate Bob's signal by crafting a similar channel. In our tests, we observe that the estimation error for Eve, caused by her wrong channel estimates, becomes significant even for short distances betwen Eve and Bob. This error results in a discrepancy between the signal generated by Eve and the one expected by Alice, and reveals Eve as an attacker.

关键词： Estimation error Protocols Transmitters Channel estimation Authentication Delays History Cryptography Underwater acoustics Multipath channels

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Streamlining Livestock Surveillance: YOLOv8 Approach to Cattle Body Segmentation

Streamlining Livestock Surveillance: YOLOv8 Approach to Catt...

引用

Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Bharti Chugh Pushepndra Kumar Upendra Mishra Himanshi Chaudhary dept. of Computer science and engineering KIET Group of institutions Ghaziabad India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

This research paper introduces an innovative approach to optimize livestock surveillance by employing YOLOv8 for cattle body segmentation. The study attained a mAP(50), mean Average Precision of 0.598 and 0.464 and mAP(50-95) on a dataset comprising 704 images. Preceding model training, extensive preprocessing and image augmentation techniques were implemented, resulting in a dataset of 1476 training, 171 validation, and 71 testing images. The developed model demonstrates considerable promise in enabling real-time mon-itoring of cattle and their health status. Additionally, to enhance its suitability for edge computing environments, quantization-aware training (QAT) abd weight compression techniques were employed, ensuring efficient deployment on resource-constrained devices. The results highlight the effectiveness and practicality of employing YOLOv8-based segmentation for livestock surveillance, with notable implications for precision agriculture and animal welfare. However, further enhancements are warranted, particularly in achieving higher mAP scores across a broader range of Intersection over Union (IoU) thresholds, to fully address the segmentation challenges in this domain.

关键词： Training Precision agriculture Animals Surveillance Computational modeling Cows Real-time systems

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Practical Challenges and Methodologies in Next Basket Recommendation (NBR)

Practical Challenges and Methodologies in Next Basket Recomm...

引用

IEEE International Conference on Electro-Information Technology

作者： Somayeh Zamani Johannes Zenkert Madjid Fathi Dept. of Electrical Engineering & Computer Science University of Siegen Siegen Germany

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Recommendation systems are evolving rapidly, with increasing emphasis on deep and sophisticated model architectures to handle complex user-item interactions, resulting in larger models for better performance. However, the deployment of these models faces challenges due to high computing requirements and latency issues. To address this, a General Bidirectional framework (BiDiGen) is proposed, integrating user preferences and item similarities for personalized and context-aware recommendations. BiDiGen employs knowledge distillation to transfer pre-trained knowledge from large teacher models to smaller student models, striking a balance between efficiency and effectiveness. The framework utilizes a bidirectional approach where teachers and students mutually learn from each other, along with hypergraph construction to capture higher-order correlations efficiently. Additionally, a denoising generator is proposed for identifying relevant items, and a sampling scheme is suggested to determine knowledge transfer between teachers and students, ultimately training them with distillation and collaborative filtering losses.

关键词： Training Correlation Computational modeling Noise reduction computer architecture Generators Information technology

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Multi-Target Integrated Sensing and Communication with MIMO In-Band Full-Duplex Systems 58

Multi-Target Integrated Sensing and Communication with MIMO ...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Chih, Yung-Chih Shen, Chung-An Celik, Abdulkadir Fouda, Mohammed E. Eltawil, Ahmed National Taiwan University of Science and Technology Electronic and Computer Engineering Dept. Taipei Taiwan King Abdullah University of Science and Technology Thuwal Saudi Arabia Rain Neuromorphics Inc. San FranciscoCA United States

ISBN: (纸本)9798350354058

In recent years, the integration of Multi-Input Multi-Output (MIMO) technology with In-Band Full-Duplex (IBFD) systems has emerged as a promising approach for multi-targets Integrated Sensing and Communication (ISAC), particularly in the context of 6G and vehicle-to-everything (V2X) communications. However, the simultaneous transmission and reception of signals in IBFD systems face significant challenges, mainly due to self-interference (SI), which severely impacts system performance. This paper presents a novel framework that combines self-interference cancellation (SIC) techniques with the MIMO-IBFD-ISAC system to enhance multi-target sensing capabilities. The framework utilizes an independent component analysis (ICA) algorithm to effectively separate self-interference from the desired signals. Additionally, a specialized multi-targets detection method is employed to recover radar signals reflected from targets accurately. Simulation results demonstrate that the proposed approach improves target detection accuracy and communication efficiency, making it a viable solution for next-generation ISAC systems. © 2024 IEEE.

关键词： Radar interference

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FMRI Data Analysis Preserving Map Variability Via Unsupervised Object-Centric Learning

FMRI Data Analysis Preserving Map Variability Via Unsupervis...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Rui Jin Seung-Jun Kim Dept. of Computer Science & Electrical Engineering University of Maryland Baltimore MD

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

A novel data-driven functional magnetic resonance imaging (fMRI) data analysis method is proposed using a deep object-centric learning paradigm. The method can faithfully estimate the variabilities in the spatial neural activation maps, which capture functional interconnections in the brain, over fMRI volumes. The key idea is to treat the component maps composing individual fMRI volumes as "objects," whose latent representations are separately learned by a set of autoencoders. Numerical tests using synthetic and real data sets verify the advantages of the proposed method compared to existing matrix factorization-based approaches.

关键词： Data analysis Functional magnetic resonance imaging engineering in medicine and biology Synthetic data

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Brain-computer Interfaces: A Key to Neural Communication's Limitless Possibilities

Brain-Computer Interfaces: A Key to Neural Communication's L...

引用

Cognitive, Green and Ubiquitous Computing (IC-CGU), International Conference on

作者： Shilpi Saxena Mritunjay Kr. Ranjan Arif Md. Sattar School of Computer Science and Engineering Sandip University Nashik India Dept. of Computer Science & Information Technology A.M. College Gaya India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

Brain-computer Interfaces (BCIs) have revolutionized human-machine interaction by establishing a direct communication pathway between the brain and an external machine. These systems rely on neural signals, acquired invasively through implanted electrodes or non-invasively using scalp sensors. Signal processing algorithms extract meaningful information from these signals, such as motor intentions or cognitive states. BCIs have applications in various domains, including healthcare, neurorehabilitation, gaming, virtual reality, and augmented reality. However, BCI development faces challenges such as improving signal acquisition and decoding techniques, enhancing reliability and robustness, and ensuring user safety and privacy. The translation of BCI technology from the lab to real-world applications requires addressing ethical, legal, and societal considerations. Standardized evaluation metrics and protocols are needed to compare and validate different BCI approaches effectively. In conclusion, BCIs have the potential to revolutionize human-machine interaction across various domains, but challenges remain in technology refinement, user acceptance, and ethical considerations. Future research directions include enhancing spatial and temporal resolution of neural signals, exploring novel electrode materials, and investigating long-term implantation methods.

关键词： Human computer interaction Electrodes Ethics Signal processing algorithms Medical services Ubiquitous computing Brain-computer interfaces

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An Improved U-Shaped Network for ROI Segmentation of Skin Lesion Images

An Improved U-Shaped Network for ROI Segmentation of Skin Le...

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Communication & Computing (WCONF), World Conference on

作者： Rasika Gupta Rajeev Kumar Dept. of Computer Science and Engineering Delhi Technological University Delhi India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Skin cancer, a serious public health concern across the world, is a deadly disease if not diagnosed at an early stage. It is often diagnosed through dermoscopic images that usually have low contrast, irregular boundaries and contains irrelevant artifacts. Due to these factors, the segmentation of Region of Interest (ROI) for effective diagnosis of this disease becomes a challenging task. However, UNet, a U-Shaped Network has proven to be an efficient segmentation model for medical image segmentation, but the result mostly suffers discrepancies with the ground truth segmented mask. Therefore, this paper introduces an improved UNet 3+ architecture with residual block at both encoder and decoder to handle gradient degradation in this deep network, incorporates attention module to focus on relevant features and reduces redundant skip connections. The improved model has been evaluated on publicly available PH2 dataset and achieved an accuracy of 96.62%, dice score of 93.84% and jaccard index of 96.79% on test data. The model surpasses other state-of-the-art UNet models for skin lesion segmentation.

关键词： Image segmentation Accuracy Computational modeling computer architecture Skin Loss measurement Lesions Public healthcare Diseases Skin cancer

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Enhanced Predictive Modeling Techniques for Early Detection of COPD Utilizing 1D Convolutional Neural Networks

Enhanced Predictive Modeling Techniques for Early Detection ...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Sneha Jha Prakash Sahu Santosh Kumar Dept. of Data Science CSVTU Chhattisgarh Computer Science & Engineering IIIT-Naya Raipur

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Artificial intelligence (AI) breakthroughs have created new opportunities in the field of medical diagnostics, especially for the early identification of respiratory conditions like Chronic Obstructive Pulmonary Disease (COPD), the most prevalent respiratory condition, is characterized by breathing difficulties, mucus (sputum) coughing, and wheezing. Early verification is critical for efficient administration. A one-dimensional convolutional neural network (1D CNN) model for COPD detection, optimized using Adam and RMSprop algorithms is proposed for this purpose. Utilizing the ICBHI 2017 dataset, the performance of each optimizer based on training and validation accuracy is evaluated. The Adam optimizer achieved a training accuracy of ${9 4 \%}$ and a validation accuracy of ${9 0 \%}$. In comparison, the RMSprop optimizer yielded a training accuracy of ${9 2 \%}$ and a validation accuracy of ${8 8 \%}$. These results demonstrated that the Adam optimizer surpassed RMSprop in terms of accuracy. It showcased the potential of the proposed 1D CNN model as a reliable diagnostic tool, contributing to the advancement of respiratory health analytics.

关键词： Training Analytical models Accuracy Computational modeling Predictive models Chronic obstructive pulmonary disease Convolutional neural networks Reliability Medical diagnosis Artificial intelligence

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Gotcha: Real-Time Video Deepfake Detection via Challenge-Response

Gotcha: Real-Time Video Deepfake Detection via Challenge-Res...

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IEEE European Symposium on Security and Privacy (EuroS&P)

作者： Govind Mittal Chinmay Hegde Nasir Memon Dept. of Computer Science and Engineering Tandon School of Engineering New York University USA

ISBN: (数字)9798350354256

ISBN: (纸本)9798350354263

With the rise of AI-enabled Real-Time Deepfakes (RTDFs), the integrity of online video interactions has become a growing concern. RTDFs have now made it feasible to replace an imposter's face with their victim in live video interactions. Such advancement in deepfakes also coaxes detection to rise to the same standard. However, existing deepfake detection techniques are asynchronous and hence ill-suited for RTDFs. To bridge this gap, we propose a challenge-response approach that establishes authenticity in live settings. We focus on talking-head style video interaction and present a taxonomy of challenges that specifically target inherent limitations of RTDF generation pipelines. We evaluate representative examples from the taxonomy by collecting a unique dataset comprising eight challenges, which consistently and visibly degrades the quality of state-of-the-art deepfake generators. These results are corroborated both by humans and a new automated scoring function, leading to 88.6% and 80.1% AUC, respectively. The findings under-score the promising potential of challenge-response systems for explainable and scalable real-time deepfake detection in practical scenarios. We provide access to data and code at https://***/mittalgovind/GOTCHA-Deepfakes.

关键词： Deepfakes Codes Taxonomy Pipelines Streaming media Real-time systems Robustness

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A Review on Deep Learning Based Question Answering with Natural Language Processing in Healthcare

A Review on Deep Learning Based Question Answering with Natu...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： Abdul Saleem Javeed C Gopala Krishnan Dept. of Computer Science & Engineering GITAM School of Technology Bengaluru INDIA

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Over the Internet, an efficient approach and promising solution to retrieve significant information envisages the beginning of Question Answering Systems (QAS). Because of data sources availability, the deep learning model based on open domain QAS focuses a substantial quantity of research work. Because of the lack of medical datasets, less attention is received in the medical domain. The deep learning (DL) utility increases the development of huge data in the healthcare domain. In the past years, popularity has grown in hardware parallelism and quick data storage availability. The challenge, pipelines, and tasks involved in medical NLP and medical imaging deploy the existing review of research in this work. The medical NLP and imaging fields deploy DL structures with its extensive survey is presented here. Over all 39 research papers are collected for this survey analysis from the years 2017 to 2023. The diagnosis is enhanced with medical imaging, Natural language processing, and DL combination to detect the appropriate combination. Each of the outcomes is shown graphically. The information provided in this survey is quite useful for newcomers carrying out research in medical informatics.

关键词： Deep learning Surveys Reviews Soft sensors Pipelines Medical services Question answering (information retrieval)

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