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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： R. Geetha M. Kanniga Parameshwari Praveena. V T. Venkatesan M. Sindhu S. Bhuvana Department of Electrical and Electronics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Information Technology RMK Engineering College Chennai Tamil Nadu India Department of CSE (AI and ML) S.A. Engineering College Chennai Tamil Nadu India Department of Mathematics R. M. D. Engineering College Chennai Tamil Nadu Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Sriram Engineering College Chennai Tamil nadu India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

For many cloud-based IoT device learning applications, including sensible clinical data analytics, phrase search—which allows retrieval of files containing a given term—plays a crucial role. In order to prevent sensitive information from being exposed by third-party providers, data owners typically encrypt files (such as medical records) before sending them to the cloud. Having said that, this does make the search operation quite challenging and current searchable encryption systems for multiple keyword searches can't do word searches because they can't determine the location connection of multiple key phrases in a query word using encrypted data stored on the cloud server side. This study presents a model called MCLPS, which stands for “Modified Cyber Law for Phrase Searching.” Its purpose is to identify specific phrases in a cloud environment. To test how well the model works, it is cross-validated with the traditional BET. The safety of information in the cloud has long been an important concern for cloud service providers and their clients, despite the fact that there are several elements that could affect data security during searches. The reason behind this is the presence of dangers posed by both external data sources and internal partners, namely, their employees. Therefore, due to its massive structure, maintaining this security perfect is an ongoing headache and difficult effort in the cloud. In most cases, this is achieved by encrypting data at all times and taking extra precautions to ensure that data is not compromised during the audition or any other internal exercises. For safe phrase search in cloud IoT, this study suggested the MCLPS model and the BET encryption technique. The data integration, recovery, indexing, sliding, encryption, decryption, and transfer processes are all encompassed in the suggested works. This proposed study introduces a new approach to encrypted searching that allows encrypted phrase searches, and the MCLPS ad BET procedures

关键词： Cloud computing Ciphers Soft sensors Keyword search Information processing Safety Servers

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Secure Data Transmission for Communication Systems Using IoT

Secure Data Transmission for Communication Systems Using IoT

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： S. Selvi L.N. Charyulu Rompicharla Chudamani Ramineni Ruchira Rawat Komala C R K. V. Kanimozhi Department of Electrical and Electronic Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Mathematics VR Siddhartha Engineering College Kanuru Andhra Pradesh India Department of Mathematics Prasad V Poturi Siddhardha Institute of Technology Vijayawada Andhra Pradesh India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Information Science and Engineering HKBK College of Engineering Bengaluru Karnataka India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

E-/m-healthcare, or electronic/mobile healthcare, has profited greatly from the widespread use of the Internet of Things (IoT), cloud computing, and wireless body-area networks (WBANs). However, there are still a number of challenges that are impeding the development of e-/m-Healthcare, including as concerns about the privacy and security of patient information. To address these concerns, a framework for healthcare systems (HES) is developed. Through a gateway, it collects medical data from WBAN s, sends it through a large wireless sensor network architecture, and makes it available in WPANs. HES also employs the Groups of Send-Receive Model (GSRM) method for key distribution, the Homomorphic Encryption Based on Matrix (HEBM) scheme for privacy protection, and an expert system that can automatically analyze the encrypted medical data and present the findings. HES is compared to other systems and approaches in terms of security, privacy, and better performance, both theoretically and empirically. Finally, the feasibility of implementing the HES prototype is analyzed.

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Non-Plasmon Resonances in the E-polarized Plane Wave Scattering and Absorption by a Circular Dielectric Wire with Partial Graphene Cover 41

Non-Plasmon Resonances in the E-polarized Plane Wave Scatter...

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41st IEEE International Conference on Electronics and Nanotechnology, ELNANO 2022

作者： Mikhailikova, Iryna Dukhopelnykov, Sergii V. Sauleau, Ronan V. N. Karazin Kharkiv National University Department of Applied Mathematics Kharkiv Ukraine Computer Science and Mathematics University of l'Aquila Department of Information Engineering L'Aquila Italy Institute of Radio-Physics and Electronics NASU Laboratory of Micro and Nano Optics L'Institut d'Electronique et des Technologies du NuméRique Universite de Rennes 1 Rennes France

ISBN: (数字)9781665469227

ISBN: (纸本)9781665469234

We analyze how the total scattering cross-section (TSCS) and absorption cross-section (ACS) of a circular dielectric rod, illuminated with the E-polarized plane wave, is modified by a conformal graphene strip of arbitrary angular width and chemical potential. In the core of our treatment, there is a dual series equation where the unknown are the field angular Fourier harmonics. We subject this equation to the Inverse Discrete Fourier Transform (IDFT) that yields a Fredholm second kind matrix equation. Such a procedure guarantees the convergence of the obtained numerical results. We present the normalized TSCS and ACS versus the angular width of graphene strip and the frequency in the range from the statics to 1 THz. These plots convincingly demonstrate that the resonances on the lowest non-plasmon natural modes of the considered scatterer are still tunable due to the tunability of graphene's conductivity. © 2022 IEEE.

关键词： Graphene

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Deep Reference Priors: What is the best way to pretrain a model?

arXiv

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arXiv 2022年

作者： Gao, Yansong Ramesh, Rahul Chaudhari, Pratik Applied Mathematics and Computational Sciences University of Pennsylvania United States Computer and Information Science University of Pennsylvania United States Electrical and Systems Engineering University of Pennsylvania United States

What is the best way to exploit extra data-be it unlabeled data from the same task, or labeled data from a related task-to learn a given task? This paper formalizes the question using the theory of reference priors. Reference priors are objective, uninformative Bayesian priors that maximize the mutual information between the task and the weights of the model. Such priors enable the task to maximally affect the Bayesian posterior, e.g., reference priors depend upon the number of samples available for learning the task and for very small sample sizes, the prior puts more probability mass on low-complexity models in the hypothesis space. This paper presents the first demonstration of reference priors for medium-scale deep networks and image-based data. We develop generalizations of reference priors and demonstrate applications to two problems. First, by using unlabeled data to compute the reference prior, we develop new Bayesian semi-supervised learning methods that remain effective even with very few samples per class. Second, by using labeled data from the source task to compute the reference prior, we develop a new pretraining method for transfer learning that allows data from the target task to maximally affect the Bayesian posterior. Empirical validation of these methods is conducted on image classification datasets. Code is available at https://***/grasp-lyrl/deep_reference_priors. © 2022, CC BY.

关键词： Classification (of information)

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STABLE PHASE RETRIEVAL FOR INFINITE DIMENSIONAL SUBSPACES OF L2(R)

arXiv

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arXiv 2022年

作者： Calderbank, Robert Daubechies, Ingrid Freeman, Daniel Freeman, Nikki Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Mathematics Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Mathematics and Statistics St Louis University St LouisMO United States Department of Biostatistics University of North Carolina at Chapel Hill Chapel HillNC United States

Phase retrieval is known to always be unstable when using a frame or continuous frame for an infinite dimensional Hilbert space. We consider a generalization of phase retrieval to the setting of subspaces of L2 which coincides with using a continuous frame for phase retrieval when the subspace is the range of the analysis operator of a continuous frame. We then prove that there do exist infinite dimensional subspaces of L2 where phase retrieval is stable. That is, we give a method for constructing an infinite dimensional subspace Y ⊆ L2 such that there exists C ≥ 1 so that min (f − gL2, f + gL2 ) ≤ Cf| − |g|L2 for all f, g ∈ Y. This construction also leads to new results on uniform stability of phase retrieval in finite dimensions. Our construction has a deterministic component and a random component. When using sub-Gaussian random variables we achieve phase retrieval with high probability and stability constant independent of the dimension n when using m on the order of n random vectors. Without sub-Gaussian or any other higher moment assumptions, we are able to achieve phase retrieval with high probability and stability constant independent of the dimension n when using m on the order of n log(n) random *** Codes 42C15, 46B42, 94A20 Copyright © 2022, The Authors. All rights reserved.

关键词： Hilbert spaces

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Matching Game for Optimized Association in Quantum Communication Networks

Matching Game for Optimized Association in Quantum Communica...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Mahdi Chehimi Bernd Simon Walid Saad Anja Klein Don Towsley Mérouane Debbah Bradley Department of Electrical and Computer Engineering Wireless@VT Virginia Tech Arlington VA USA Communication Engineering Lab Technische Universität Darmstadt Darmstadt Germany Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon College of Information and Computer Sciences University of Massachusetts Amherst Amherst MA USA Electrical Engineering and Computer Science Department Khalifa University of Science and Technology Abu Dhabi UAE

Enabling quantum switches (QSs) to serve requests submitted by quantum end nodes in quantum communication networks (QCNs) is a challenging problem due to the heterogeneous fidelity requirements of the submitted requests and the limited resources of the QCN. Effectively determining which requests are served by a given QS is fundamental to foster developments in practical QCN applications, like quantum data centers. However, the state-of-the-art on QS operation has overlooked this association problem, and it mainly focused on QCNs with a single QS. In this paper, the request-QS association problem in QCNs is formulated as a matching game that captures the limited QCN resources, heterogeneous application-specific fidelity requirements, and scheduling of the different QS operations. To solve this game, a swap-stable request-QS association (RQSA) algorithm is proposed while considering partial QCN information availability. Extensive simulations are conducted to validate the effectiveness of the proposed RQSA algorithm. Simulation results show that the proposed RQSA algorithm achieves a near-optimal (within 5%) performance in terms of the percentage of served requests and overall achieved fidelity, while outperforming benchmark greedy solutions by over 13%. Moreover, the proposed RQSA algorithm is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.

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Integrated phononic waveguides in diamond

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Physical Review applied 2024年第1期21卷 014034-014034页

作者： Sophie Weiyi Ding Benjamin Pingault Linbo Shao Neil Sinclair Bartholomeus Machielse Cleaven Chia Smarak Maity Marko Lončar John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA QuTech & Kavli Institute of Nanoscience Delft University of Technology PO Box 5046 2600 Georgia Delft Netherlands Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg Virginia 24061 USA AWS Center for Quantum Networking Boston Massachusetts 02135 USA

Efficient generation, guiding, and detection of phonons, or mechanical vibrations, are of interest in various fields, including radio-frequency communication, sensing, and quantum information. Diamond is a useful platform for phononics because of the presence of strain-sensitive spin qubits, and its high Young’s modulus, which allows for low-loss gigahertz devices. We demonstrate a diamond phononic waveguide platform for generating, guiding, and detecting gigahertz-frequency surface acoustic wave (SAW) phonons. We generate SAWs using interdigital transducers integrated on AlN/diamond and observe SAW transmission at 4–5 GHz through both ridge and suspended waveguides, with wavelength-scale cross sections (approximately 1 m2) to maximize spin-phonon interaction. This work is a crucial step for developing acoustic components for quantum phononic circuits with strain-sensitive color centers in diamond.

关键词： Acoustic wave phenomena Coherent control Phonons Quantum information with hybrid systems Diamond Nanomechanical devices Nitrogen vacancy centers in diamond Spin

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Loop-Diffusion: an equivariant diffusion model for designing and scoring protein loops

arXiv

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arXiv 2024年

作者： Borisiak, Kevin Visani, Gian Marco Nourmohammad, Armita Department of Physics University of Washington United States Paul G. Allen School of Computer Science and Engineering University of Washington United States Department of Applied Mathematics University of Washington United States Fred Hutch Cancer Research Center SeattleWA United States

Predicting protein functional characteristics from structure remains a central problem in protein science, with broad implications from understanding the mechanisms of disease to designing novel therapeutics. Unfortunately, current machine learning methods are limited by scarce and biased experimental data, and physics-based methods are either too slow to be useful, or too simplified to be accurate. In this work, we present Loop-Diffusion, an energy based diffusion model which leverages a dataset of general protein loops from the entire protein universe to learn an energy function that generalizes to functional prediction tasks. We evaluate Loop-Diffusion’s performance on scoring TCR-pMHC interfaces and demonstrate state-of-the-art results in recognizing binding-enhancing mutations. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning systems

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Efficient and Robust Continual Graph Learning for Graph Classification in Biology

arXiv

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arXiv 2024年

作者： Zhang, Ding Downer, Jane Chen, Can Wang, Ren The Department of Computer Science Northwestern University EvanstonIL60208 United States The Department of Computer Science Illinois Institute of Technology ChicagoIL60616 United States The School of Data Science and Society The Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States The Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Graph classification is essential for understanding complex biological systems, where molecular structures and interactions are naturally represented as graphs. Traditional graph neural networks (GNNs) perform well on static tasks but struggle in dynamic settings due to catastrophic forgetting. We present Perturbed and Sparsified Continual Graph Learning (PSCGL), a robust and efficient continual graph learning framework for graph data classification, specifically targeting biological datasets. We introduce a perturbed sampling strategy to identify critical data points that contribute to model learning and a motif-based graph sparsification technique to reduce storage needs while maintaining performance. Additionally, our PSCGL framework inherently defends against graph backdoor attacks, which is crucial for applications in sensitive biological contexts. Extensive experiments on biological datasets demonstrate that PSCGL not only retains knowledge across tasks but also enhances the efficiency and robustness of graph classification models in biology. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Vinod Kumar Isha Jain Durga Sritha Dongla Shivangi Rai Vani Rastogi Eva Kaushik Rohit Kaushik School of Engineering & Applied Science The George Washington University Washington D.C USA Computer Science & Engineering Department Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Computer Science & Engineering Department Gandhi Institute of Technology and Management Visakhapatnam Andhra Pradesh Data Scientist DXC Technology Noida India CS and Mathematics Department Data Analytics Program University of Illinois Springfield USA

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions.

关键词： Deep learning Pathology Accuracy Melanoma computer architecture Benchmark testing Skin Convolutional neural networks Lesions Medical diagnosis

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