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作者： Woo, Jiheon Yoo, Chanhee Kim, Young-Sik Cassuto, Yuval Kim, Yongjune Department of Electrical Engineering Pohang37673 Korea Republic of Department of Electrical Engineering and Computer Science Daegu42988 Korea Republic of Chosun University Department of Information and Communication Engineering Gwangju61452 Korea Republic of Technion-Israel Institute of Technology Viterbi Department of Electrical and Computer Engineering Haifa32000 Israel Institute for Convergence Research and Education in Advanced Technology Yonsei University Seoul03722 Korea Republic of

The min-entropy is a widely used metric to quantify the randomness of generated random numbers, which measures the difficulty of guessing the most likely output. It is difficult to accurately estimate the min-entropy of a non-independent and identically distributed (non-IID) source. Hence, NIST Special Publication (SP) 800-90B adopts ten different min-entropy estimators and then conservatively selects the minimum value among these ten min-entropy estimates. Among these estimators, the longest repeated substring (LRS) estimator estimates the collision entropy instead of the min-entropy by counting the number of repeated substrings. Since the collision entropy is an upper bound on the min-entropy, the LRS estimator inherently provides overestimated outputs. In this paper, we propose two techniques to estimate the min-entropy of a non-IID source accurately. The first technique resolves the overestimation problem by translating the collision entropy into the min-entropy. Next, we generalize the LRS estimator by adopting the general Rényi entropy instead of the collision entropy (i.e., Rényi entropy of order two). We show that adopting a higher order can reduce the variance of min-entropy estimates. By integrating these techniques, we propose a generalized LRS estimator that effectively resolves the overestimation problem and provides stable min-entropy estimates. Theoretical analysis and empirical results support that the proposed generalized LRS estimator improves the estimation accuracy significantly, which makes it an appealing alternative to the LRS estimator. © 2005-2012 IEEE.

关键词： Frequency estimation

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Assessing a BERT-based model for analyzing subjectivity and classifying academic articles

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Multimedia Tools and Applications 2024年第42期83卷 90511-90532页

作者： Mehmood, Atif Shahid, Farah Khan, Rizwan Ahmed, Shahzad Ibrahim, Mostafa M. Zheng, Zhonglong School of Computer Science and Technology Zhejiang Normal University Jinhua321002 China Zhejiang Institute of Photoelectronics & Zhejiang Institute for Advanced Light Source Zhejiang Normal University Zhejiang Jinhua321004 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt

The metaverse concept extends beyond virtual worlds and can be applied to collaborative analysis environments. Data analysts worldwide may read academic article extracts in real-time in a shared digital workplace to mine and analyze data using the metaverse. Furthermore, a semantic metaverse in natural language processing might also involve creating a digital environment with linguistic and semantic connections. Many dedicated researchers navigating the complexities of natural language processing in the metaverse era have spent considerable time searching for relevant papers. However, online reviews and evaluations of articles are helpful for their assistance and may save the researcher time. In this work, human specialists manually produced a dataset from four conferences and evaluated subjectively using rule-based techniques. Subsequently, we aim to evaluate the effectiveness of pre-trained word embeddings and pre-trained BERT models seamlessly integrated with convolutional neural networks. This endeavor focuses on the subjective analysis and classification of contributions and previous work sentences extracted from academic literature. For comparison, various deep learning architectures were systematically employed, including long short-term memory-GloVe and bi-directional long short-term memory-GloVe, alongside classical machine learning methods. Our findings show that the proposed BERT model achieved state-of-the-art performance in classification and subjective analysis tasks with an accuracy of 91.50% and F1 score of 91.00%. Finally, we plan to utilize sentence similarity to identify contributions within abstracts, thus outlining potential avenues for future research. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Natural language processing systems

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On Achieving High-Fidelity Grant-free Non-Orthogonal Multiple Access

arXiv

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arXiv 2024年

作者： Mei, Haoran Peng, Limei Ho, Pin-Han The School of Computer Science and Engineering Kyungpook National University Deagu Korea Republic of Shenzhen Institute for Advanced Study UESTC Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

Grant-free access (GFA) has been envisioned to play an active role in massive Machine Type Communication (mMTC) under 5G and Beyond mobile systems, which targets at achieving significant reduction of signaling overhead and access latency in the presence of sporadic traffic and small-size data. The paper focuses on a novel K-repetition GFA (K-GFA) scheme by incorporating Reed-Solomon (RS) code with the contention resolution diversity slotted ALOHA (CRDSA), aiming to achieve high-reliability and low-latency access in the presence of massive uncoordinated MTC devices (MTCDs). We firstly defines a MAC layer transmission structure at each MTCD for supporting message-level RS coding on a data message of Q packets, where a RS code of KQ packets is generated and sent in a super time frame (STF) that is composed of Q time frames. The access point (AP) can recover the original Q packets of the data message if at least Q out of the KQ packets of the RS code are successfully received. The AP buffers the received MTCD signals of each resource block (RB) within an STF and exercises the CRDSA based multi-user detection (MUD) by exploring signal-level inter-RB correlation via iterative interference cancellation (IIC). With the proposed CRDSA based K-GFA scheme, we provide the complexity analysis, and derive a closed-form analytical model on the access probability for each MTCD as well as its simplified approximate form. Extensive numerical experiments are conducted to validate its effectiveness on the proposed CRDSA based K-GFA scheme and gain deep understanding on its performance regarding various key operational parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiuser detection

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On Achieving High-Fidelity Grant-Free Non-Orthogonal Multiple Acces

SSRN

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SSRN 2024年

作者： Peng, Limei Mei, Haoran Ho, Pin-Han The School of Computer Science and Engineering Kyungpook National University Deagu Korea Republic of Shenzhen Institute for Advanced Study UESTC Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

关键词： Multiuser detection

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A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance

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Neural Computing and Applications 2024年第30期36卷 18979-18997页

作者： Natarajan, Krishnamoorthy Muthusamy, Suresh Sha, Mizaj Shabil Sadasivuni, Kishor Kumar Sekaran, Sreejith Charles Gnanakkan, Christober Asir Rajan A.Elngar, Ahmed School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Tamil Nadu Perundurai Erode India Centre for Advanced Materials Qatar University Doha Qatar Department of Mechanical and Industrial Engineering Qatar University PO Box 2713 Doha Qatar Department of Electrical Engineering National Institute of Technology Assam Silchar India Department of Electrical and Electronics Engineering Puducherry Technological University Puducherry India Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc., © The Author(s) 2024.

关键词： Diseases

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A Novel GNN-based Decoding Scheme for Sparse Code Multiple Access (SCMA)

A Novel GNN-based Decoding Scheme for Sparse Code Multiple A...

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International Conference on Networking and Network Applications (NaNA)

作者： ZiJian Chen Limei Peng Pin-Han Ho School of Computer Science and Engineering Kyungpook National University Deagu South Korea Department of Electrical and Computer Engineering University of Waterloo Ontario Canada Shenzhen Institute for Advanced Study UESTC

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

Sparse Code Multiple Access (SCMA) is a promising code-based multiple access technique for achieving higher spectral efficiency and massive connectivity that is crucially required in the B5G applications such as massive machine-type communication (mMTC). Traditional SCMA receivers use Maximum Likelihood (ML) and Message Passing Algorithm (MPA) for signal decoding, which nonetheless suffer from extremely high computational complexity. To resolve the issue, we propose to use Graph Neural Networks (GNN) to replace MPA for decoding, aiming at reducing the decoding complexity while maintaining satisfactory Bit Error Rate (BER) performance. Simulation results show that our proposed solution can achieve much higher decoding accuracy and faster decoding speed.

关键词： Codes Accuracy Spectral efficiency Simulation Bit error rate Receivers Parallel processing

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Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning 42

Adaptive Configuration for Heterogeneous Participants in Dec...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Liao, Yunming Xu, Yang Xu, Hongli Wang, Lun Qian, Chen University of Science and Technology of China School of Computer Science and Technology China University of Science and Technology of China Suzhou Institute for Advanced Study China University of California Jack Baskin School of Engineering Department of Computer Science and Engineering Santa Cru United States

ISBN: (纸本)9798350334142

Data generated at the network edge can be processed locally by leveraging the paradigm of edge computing (EC). Aided by EC, decentralized federated learning (DFL), which overcomes the single-point-of-failure problem in the parameter server (PS) based federated learning, is becoming a practical and popular approach for machine learning over distributed data. However, DFL faces two critical challenges, i.e., system heterogeneity and statistical heterogeneity introduced by edge devices. To ensure fast convergence with the existence of slow edge devices, we present an efficient DFL method, termed FedHP, which integrates adaptive control of both local updating frequency and network topology to better support the heterogeneous participants. We establish a theoretical relationship between local updating frequency and network topology regarding model training performance and obtain a convergence upper bound. Upon this, we propose an optimization algorithm, that adaptively determines local updating frequencies and constructs the network topology, so as to speed up convergence and improve the model accuracy. Evaluation results show that the proposed FedHP can reduce the completion time by about 51% and improve model accuracy by at least 5% in heterogeneous scenarios, compared with the baselines. © 2023 IEEE.

关键词： Edge computing

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Lithographic printing inspired in-situ transfer of MXene-based films with localized topo-electro tunability for high-performance flexible pressure sensors

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Nano Research 2023年第11期16卷 12670-12679页

作者： Qiuyang Yan Yi Zhou Yin Cheng Liangjing Shi Ranran Wang Lian Gao Jing Sun State Key Laboratory of High Performance Ceramics and Superfine Microstructure Shanghai Institute of CeramicsChinese Academy of ScienceShanghai 200050China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore School of Chemistry and Materials Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

MXene-based films have been intensively explored for construction of piezoresistive flexible pressure sensors owing to their excellent mechanical and electrical *** pressure sensitivity relies on pre-molding a flexible substrate,or regulating the micromorphology of MXene sheets,to obtain a micro-structured ***,the two avenues usually require complicated and time-consuming microfabrication or wet chemical processing,and are limited to non-adjustable topographicelectrical(topo-electro)***,we propose a lithographic printing inspired in-situ transfer(LIPIT)strategy to fabricate MXene-ink films(MIFs).In LIPIT,MIFs not only inherit ridge-and-valley microstructure from paper substrate,but also achieve localized topo-electro tunability by programming ink-writing patterns and *** MIF-based flexible pressure sensor with periodical topo-electro gradient exhibits remarkably boosted sensitivity in a wide sensing range(low detection limit of 0.29 Pa and working range of 100 kPa).The MIF sensor demonstrates versatile applicability in both subtle and vigorous pressuresensing fields,ranging from pulse wave extraction and machine learning-assisted surface texture recognition to piano-training glove(PT-glove)for piano *** LIPIT is quick,low-cost,and compatible with free ink/substrate combinations,which promises a versatile toolbox for designing functional MXene films with tailored morphological-mechanical-electrical properties for extended application scenarios.

关键词： MXene film transfer printing surface tunability flexible pressure sensor

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A Fast Algorithm for Finding the Roots of Polynomials over Finite Fields 15

A Fast Algorithm for Finding the Roots of Polynomials over F...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Li, Pingping Yu, Leilei Song, Linqi Hou, Hanxu Dongguan University of Technology School of Computer Science & Technology Dongguan China University of Electronic Science & Technology of China Shenzhen Institute for Advanced Study China City University of Hong Kong Department of Computer Science Hong Kong Dongguan University of Technology School of Electrical Engineering & Intelligentization Dongguan China

ISBN: (纸本)9798350324662

Finding all roots of polynomials over finite fields is a crucial problem in algebraic coding techniques, such as decoding BCH codes and Reed-Solomon codes. The current commonly used methods are Chien search or fast Fourier transform (FFT), which evaluates all elements in a finite field and results in field multiplications whose number increases linearly with the field size. In this paper, we propose a new approach that enables the multiplicative complexity to grow sub-linearly with the field size. To our knowledge, this is the currently lowest multiplicative complexity for this problem. Additionally, the proposed approach maintains the additive complexity similar to that of FFT-based method. © 2023 IEEE.

关键词： error-correction codes error-locator polynomial fast Fourier transforms (FFT) Polynomial evaluation

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Efficient simulations of Hartree-Fock equations by an accelerated gradient descent method

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Physical Review E 2024年第5期110卷 055304页

作者： Y. Ohno A. Del Maestro T. I. Lakoba Department of Mathematics and Statistics Department of Physics and Astronomy Min H. Kao Department of Electrical Engineering and Computer Science Institute for Advanced Materials & Manufacturing

We develop convergence acceleration procedures that enable a gradient descent-type iteration method to efficiently simulate Hartree-Fock equations for many particles interacting both with each other and with an external potential. Our development focuses on three aspects: (i) optimization of a parameter in the preconditioning operator; (ii) adoption of a technique that eliminates the slowest-decaying mode to the case of many equations (describing many particles); and (iii) a novel extension of the above technique that allows one to eliminate multiple modes simultaneously. We illustrate performance of the numerical method for the two-dimensional model of the first layer of helium atoms above a graphene sheet. We demonstrate that incorporation of aspects (i) and (ii) above into the “plain” gradient descent method accelerates it by at least two orders of magnitude, and often by much more. Aspect (iii)—the multiple-mode elimination—may bring further improvement to the convergence rate compared to aspect (ii), the single-mode elimination. Both single- and multiple-mode elimination techniques are shown to significantly outperform the well-known Anderson Acceleration. We believe that our acceleration techniques can also be employed by other iterative methods, especially those handling hard-core-type interaction of many particles.

关键词： First-principles calculations Hartree-Fock methods Numerical techniques

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