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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Baker El-Ebiary, Yousef A. Hatamleh, Amer Aseh, Khairi Altrad, Abdullah Amayreh, Khalid Thaher Hilles, Shadi Mohamed, Rajina R. Ravindran Pathmanathan, P. Aledinat, Lowai Saleh UniSZA Faculty of Informatics and Computing Malaysia Irbid National University Faculty of Financial and Business Science Information System Department Irbid Jordan International Institute of Applied Science Swiss School of Management Netherlands Al Madinah International University Faculty of Computer and Info. Tech Malaysia Irbid National University Faculty of Financial and Business Science Administration Department Irbid Jordan Istanbul Okan University Department of Software Engineering Turkey Universiti Tenaga Nasional College of Computing Dan Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia

ISBN: (纸本)9781665432221

This research introduces the distributed ledger technology (blockchain) in terms of sustainable development in the future. This study is based on private, public and global communications in various industries. The growing demands for secure and decentralized transactions around the world demonstrate the need for Blockchain technology as a medium of operations and communications. The blockchain system is completely decentralized and allows users to exchange messages in an efficient and secure manner. The highlight of the paper is the anticipated future use of blockchain networks as a communication tool in every business and digital economy for a safe and sustainable development, while presenting its advantages and limitations that have been confirmed based on the experiences of various experts. Moreover, the article provides recommendations for the use of blockchain to greatly complicate its identification in future communications. The paper also concludes that the use of the blockchain system could be beneficial and enhance future communications and digital technologies. © 2021 IEEE.

关键词： Blockchain

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Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime

arXiv

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arXiv 2022年

作者： Alghamdi, Wael Asoodeh, Shahab Calmon, Flavio P. Kosut, Oliver Sankar, Lalitha Wei, Fei The School of Engineering and Applied Science Harvard University United States The Department of Computing and Software McMaster University Canada The School of Electrical Computer and Energy Engineering Arizona State University United States

Most differential privacy mechanisms are applied (i.e., composed) numerous times on sensitive data. We study the design of optimal differential privacy mechanisms in the limit of a large number of compositions. As a consequence of the law of large numbers, in this regime the best privacy mechanism is the one that minimizes the Kullback-Leibler divergence between the conditional output distributions of the mechanism given two different inputs. We formulate an optimization problem to minimize this divergence subject to a cost constraint on the noise. We first prove that additive mechanisms are optimal. Since the optimization problem is infinite dimensional, it cannot be solved directly;nevertheless, we quantize the problem to derive near-optimal additive mechanisms that we call "cactus mechanisms" due to their shape. We show that our quantization approach can be arbitrarily close to an optimal mechanism. Surprisingly, for quadratic cost, the Gaussian mechanism is strictly sub-optimal compared to this cactus mechanism. Finally, we provide numerical results which indicate that cactus mechanism outperforms the Gaussian mechanism for a finite number of compositions. © 2022, CC BY.

关键词： Additives

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Direct GPU Compilation and Execution for Host Applications with OpenMP Parallelism

Direct GPU Compilation and Execution for Host Applications w...

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LLVM Compiler Infrastructure in HPC (LLVM-HPC)

作者： Shilei Tian Joseph Huber Konstantinos Parasyris Barbara Chapman Johannes Doerfert Department of Computer Science Stony Brook University Stony Brook USA Advanced Micro Devices Austin USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore USA

ISBN: (纸本)9781665475594

Currently, offloading to accelerators requires users to identify which regions are to be executed on the device, what memory needs to be transferred, and how synchronization is to be resolved. On top of these manual tasks, many standard (C/ C++ library) functions, such as file I/O or memory manipulation, cannot be directly executed on the device and need to be worked around by the user explicitly. This makes it challenging to port programs in the first place and hinders developers from testing features on the GPU and within the GPU compilation pipeline. Existing tests and test suites for the host are effectively unusable for accelerators and need to be manually ported to provide the same benefits for the devices as they do on the host. In this paper, we propose a direct GPU compilation scheme that leverages the portable target offloading interface provided by LLVM/OpenMP. Utilizing this infrastructure allows us to compile an existing host application for the GPU and execute it there with only a minimal wrapper layer for the user code, command line arguments, and a compiler provided GPU implementation of C/ C++ standard library functions. The C/ C++ library functions are partially implemented for direct device execution and otherwise fallback to remote procedure call (RPC) to call host functions transparently. Our proposed prototype will allow users to quickly compile for, and test on, the GPU without explicitly handling kernel launches, data mapping, or host-device synchronization. We evaluate our implementation using three proxy applications with host OpenMP parallelism and three microbenchmarks to test the correctness of our prototype GPU compilation.

关键词： Pipelines Graphics processing units Prototypes C++ languages Parallel processing Libraries Synchronization

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Analyzing Prospects for Quantum Advantage in Topological Data Analysis

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PRX Quantum 2024年第1期5卷 010319-010319页

作者： Dominic W. Berry Yuan Su Casper Gyurik Robbie King Joao Basso Alexander Del Toro Barba Abhishek Rajput Nathan Wiebe Vedran Dunjko Ryan Babbush School of Mathematical and Physical Sciences Macquarie University Sydney NSW 2109 Australia Google Quantum AI Venice California 90291 USA applied Quantum algorithms (aQa) Leiden University Leiden 2300 RA Netherlands Department of Computing and Mathematical Sciences Caltech Pasadena California 91125 USA Department of Computer Science University of Toronto Ontario M5S 2E4 Canada Pacific Northwest National Laboratory Richland Washington 99354 USA

Lloyd et al. [Nat. Commun. 7, 10138 (2016)] were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that superquadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for superpolynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where superpolynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.

关键词： Quantum algorithms & computation

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Transmission Line Fault Detection and Classification Using Feature Extraction Based Self-Attention Convolutional Neural Network with Time Series Image

Transmission Line Fault Detection and Classification Using F...

引用

Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Muntather Almusawi D. Raja Babu Debarshi Mazumder Bala Dhandayuthapani V. N. Naga Saranya Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas campus Oman Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The transmission lines continuously experience the number of shunt faults and its effective in practical system increases the instability, load damages and line restoration cost. This work implements an advanced self-attention convolutional neural network (SAT-CNN) method for fault detection and classification (FDC) of high voltage transmission lines experiencing a constant number of shunt faults. Faults cause load damage in real-time applications, increase instability, and increase the cost of line restoration. Therefore, a precise model is needs to identify and categorize the flaws to quickly restore he problematic phases. In this research, implemented SAT-CNN feature extraction model with imaging-based on time series that can accurately detect and classify faults. By employing a number of input signals, including, current, voltage, and combined current and voltage signals, at different sampling frequencies, the efficacy of implemented SAT-CNN model is evaluated. Implemented SAT-CNN method obtains high performance when compared to existing methods including weight-sharing network (WSCN), Truncated singular value decomposition and Human urbanization algorithm based Recurrent Perceptron Neural Network (TSVD-HUARPNN), and Graph convolutional neural network (GCN), and result achieved 99.90% accuracy value.

关键词： Power transmission lines Costs Fault detection Time series analysis Urban areas Feature extraction Transmission line measurements

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INFORMATION FRAGILITY OR ROBUSTNESS UNDER QUANTUM CHANNELS

arXiv

引用

arXiv 2023年

作者： Laracuente, Nicholas Smith, Graeme Department of Computer Science University of Indiana Bloomington 700 N Woodlawn Ave BloomingtonIN47408 United States Institute for Quantum Computing Department of Applied Mathematics University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada

Quantum states naturally decay under noise. Many earlier works have quantified and demonstrated lower bounds on the decay rate, showing exponential decay in a wide variety of contexts. Here we study the converse question: are there uniform upper bounds on the ratio of post-noise to initial information quantities when noise is sufficiently weak? In several scenarios, including classical, we find multiplicative converse bounds. However, this is not always the case. Even for simple noise such as qubit dephasing or depolarizing, mutual information may fall by an unbounded factor under arbitrarily weak noise. As an application, we find families of channels with non-zero private capacity despite arbitrarily high probability of transmitting an arbitrarily good copy of the input to the environment. Copyright © 2023, The Authors. All rights reserved.

关键词： Decay (organic)

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The Saddle-Point Accountant for Differential Privacy

arXiv

引用

arXiv 2022年

作者： Alghamdi, Wael Asoodeh, Shahab Calmon, Flavio P. Gomez, Juan Felipe Kosut, Oliver Sankar, Lalitha Wei, Fei The School of Engineering and Applied Science Harvard University United States The Department of Computing and Software McMaster University Canada The School of Electrical Computer and Energy Engineering Arizona State University United States

We introduce a new differential privacy (DP) accountant called the saddle-point accountant (SPA). SPA approximates privacy guarantees for the composition of DP mechanisms in an accurate and fast manner. Our approach is inspired by the saddle-point method-a ubiquitous numerical technique in statistics. We prove rigorous performance guarantees by deriving upper and lower bounds for the approximation error offered by SPA. The crux of SPA is a combination of large-deviation methods with central limit theorems, which we derive via exponentially tilting the privacy loss random variables corresponding to the DP mechanisms. One key advantage of SPA is that it runs in constant time for the n-fold composition of a privacy mechanism. Numerical experiments demonstrate that SPA achieves comparable accuracy to state-of-the-art accounting methods with a faster runtime. © 2022, CC BY.

关键词： Numerical methods

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THE INTERPLAY BETWEEN PHYSICAL ACTIVITY, PROTEIN CONSUMPTION, AND SLEEP QUALITY IN MUSCLE PROTEIN SYNTHESIS

arXiv

引用

arXiv 2024年

作者： Devkota, Ayush Gautam, Manakamana Dhakal, Uttam Devkota, Suman Gupta, Gaurav Kumar Nepal, Ujjwal Dhuru, Amey Dinesh Singh, Aniket Kumar Department of Applied Science and Chemical Engineering Pulchowk Engineering College Lalitpur Nepal School of Health and Allied Sciences Pokhara University Nepal Department of Electrical and Computer Engineering Youngstown State University OH United States Department of Computing and Information Systems Youngstown State University OH United States Department of Computer Science and Information Systems Youngstown State University OH United States Maharishi International University FairfieldIA United States State University of New York at Buffalo BuffaloNY United States

This systematic review examines the synergistic and individual influences of resistance exercise, dietary protein supplementation, and sleep/recovery on muscle protein synthesis (MPS). Electronic databases such as Scopus, Google Scholar, and Web of science were extensively used. Studies were selected based on relevance to the criteria and were ensured to be directly applicable to the objectives. Research indicates that a protein dose of 20 to 25 grams maximally stimulates MPS post-resistance training. It is observed that physically frail individuals aged 76 to 92 and middle-aged adults aged 62 to 74 have lower mixed muscle protein synthetic rates than individuals aged 20 to 32. High-whey protein and leucine-enriched supplements enhance MPS more efficiently than standard dairy products in older adults engaged in resistance programs. Similarly, protein intake before sleep boosts overnight MPS rates, which helps prevent muscle loss associated with sleep debt, exercise-induced damage, and muscle-wasting conditions like sarcopenia and cachexia. Resistance exercise is a functional intervention to achieve muscular adaptation and improve function. Future research should focus on variables such as fluctuating fitness levels, age groups, genetics, and lifestyle factors to generate more accurate and beneficial results. © 2024, CC BY.

关键词： Sleep research

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MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

arXiv

引用

arXiv 2023年

作者： Chen, Cheng Miao, Juzheng Wu, Dufan Yan, Zhiling Kim, Sekeun Hu, Jiang Zhong, Aoxiao Liu, Zhengliang Sun, Lichao Li, Xiang Liu, Tianming Heng, Pheng-Ann Li, Quanzheng Center of Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering Lehigh University BethlehemPA18015 United States Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States School of Computing The University of Georgia AthensGA30602 United States

The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks. However, SAM’s performance significantly declines when applied to medical images, primarily due to the substantial disparity between natural and medical image domains. To effectively adapt SAM to medical images, it is important to incorporate critical third-dimensional information, i.e., volumetric or temporal knowledge, during fine-tuning. Simultaneously, we aim to harness SAM’s pre-trained weights within its original 2D backbone to the fullest extent. In this paper, we introduce a modality-agnostic SAM adaptation framework, named as MA-SAM, that is applicable to various volumetric and video medical data. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments while preserving the majority of SAM’s pre-trained weights. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data. The effectiveness of our method has been comprehensively evaluated on four medical image segmentation tasks, by using 10 public datasets across CT, MRI, and surgical video data. Remarkably, without using any prompt, our method consistently outperforms various state-of-the-art 3D approaches, surpassing nnU-Net by 0.9%, 2.6%, and 9.9% in Dice for CT multi-organ segmentation, MRI prostate segmentation, and surgical scene segmentation respectively. Our model also demonstrates strong generalization, and excels in challenging tumor segmentation when prompts are used. Our code is available at: https://***/cchen-cc/MA-SAM. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Machinery Fault Diagnoses in High-Voltage Circuit Breakers Using Empirical Mode Decomposition-Support Vector Machine Model

Machinery Fault Diagnoses in High-Voltage Circuit Breakers U...

引用

Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Mohammed Al-Farouni A.V.V. Sudhakar Divya G Bala Dhandayuthapani V. Gandla Shivakanth Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas campus Shinas Oman Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad Telangana India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The circuit breakers have a flaw in their trip mechanism. Early mechanical faults like coil-plunger jam are hard to report using traditional fault diagnosis based on the coil current. However, users still have difficulty understanding and trusting the diagnosis results since the standard EMD is opaque in terms of interpretability. In this work, an Empirical Mode Decomposition with Support Vector Machine (EMP-SVM) proposed for machinery fault diagnosis. This work uses the vibration signal recovered from the trip process as an intrinsic time series of the data. At first, The Improved EMD is a data-adaptive multiresolution approach that breaks down signals into physically relevant components based on vibration and coil current signals. Lastly, utilizing the characteristics that were retrieved, the SVM is used to diagnose the defect. The implemented improved EMP-SVM model demonstrates superior performance, achieving Precision of (98.37 % ), recall of (91.03%) and F1-Score of (96.34%). When compared to previous models like Improved Dempster-Shafer Evidence Theory-Echo State Neural Network (IDSET-ESNN) and Phase Space Reconstruction with Support Vector Machine (PSR-SVM).

关键词： Support vector machines Vibrations Computational modeling Circuit breakers High-voltage techniques Vectors Circuit faults

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