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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Ponnila P Bazila Banu A Professor Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India Department of Computer Science and Business Systems KPR Institute of Engineering and Technology Coimbatore Tamilnadu India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Plant diseases are a biggest challenge for the farmers. Agriculture relies heavily on its production, and diagnosis of plant disease in an accurate manner will help in identification of the appropriate disease. If correct identification was not performed, there might be a significant financial loss that could have an impact on both farmers and the national economy. For resolving this issue, the AlexNet was employed, a convolutional neural network method. In this research, AlexNet is used to classify the diseases occurring in chilli, cauliflower and sunflower. This AlexNet model, with Stochastic Gradient Descent(SGD) algorithm optimization techniques had achieved 82% accuracy for the overall classification of the test images of chilli plant, 98% accuracy for the overall classification of the test images of cauliflower plant and 91% accuracy for the overall classification of the test images of sunflower plant; with Adadelta optimization 86%, 99% and 93% accuracy was achieved for the overall classification of the test pictures of chilli plant, cauliflower plant and sunflower plant respectively. After using the Asynchronous Stochastic Gradient(ASGD) optimization with AlexNet model, the accuracy obtained are 88% for the test images of chilli plant, 98% for the test images of cauliflower plant and 91% for the test images of sunflower plant; and finally, AdaGrad optimization with AlexNet achieved accuracy of 80% for the test images of chilli plant, 9 8% for the test images of cauliflower plant and 93% for the test images of sunflower plant.

关键词： Training Plant diseases Visualization Accuracy Image resolution Stochastic processes Production Feature extraction Robustness Optimization

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Robust Reversible Audio Watermarking Scheme for Telemedicine and Privacy Protection

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computers, Materials & Continua 2022年第5期71卷 3035-3050页

作者： Xiaorui Zhang Xun Sun Xingming Sun Wei Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

The leakage of medical audio data in telemedicine seriously violates the privacy of *** order to avoid the leakage of patient information in telemedicine,a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio *** scheme decomposes the medical audio into two independent embedding domains,embeds the robust watermark and the reversible watermark into the two domains *** order to ensure the audio quality,the Hurst exponent is used to find a suitable position for watermark *** to the independence of the two embedding domains,the embedding of the second-stage reversible watermark will not affect the first-stage watermark,so the robustness of the first-stage watermark can be well *** the second stage,the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible *** experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db,additive white Gaussian noise(AWGN)of 20 db,low-pass filtering,resampling,re-quantization and other attacks,and has good imperceptibility.

关键词： Telemedicine privacy protection audio watermarking robust reversible watermarking two-stage embedding

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FORTUNATE: Decrypting and Classifying Malware by Variable Length Instruction Sequences 13

FORTUNATE: Decrypting and Classifying Malware by Variable Le...

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13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Andrade, Cesar A. B. Rocha Filho, Geraldo P. Meneguette, Rodolfo I. Maranhao, Joao Paulo A. Sant'ana, Ricardo Duarte, Julio Cesar Serrano, Andre Luiz M. Goncalves, Vinicius P. University of Brasília Electrical Engineering Dept. Brasília Brazil State University of Southwest Bahia Exact and Technological Sciences Dept. Vitória da Conquista Brazil Institute of Mathematics and Computer Science Universityof São Paulo São Carlos Brazil Brazilian Army Systems Development Center Brasília Brazil Military Institute of Engineering Defense Engineering Dept. Rio de Janeiro Brazil Military Institute of Engineering Computing Engineering Dept. Rio de Janeiro Brazil University of Brasília Production Engineering Dept. Brasília Brazil

ISBN: (纸本)9798350376562

After identifying an artifact as malware, the additional challenge arises of correctly classifying it into a specific family. This raises the challenge of classifying malware amid growing complexity and the increasing volume of cyber threats. Therefore, we introduce FORTUNATE, a framework that employs variable-length instruction sequences to classify malware more efficiently and accurately, focusing on real and active malware. By introducing effective methods for extracting opcodes and their representation in the smallest possible vectors, FORTUNATE surpasses the limitations of previous approaches, enabling the correct identification of malware in their respective families, while avoiding redundancies in the data. The results achieved with FORTUNATE demonstrate advances in both precision and computational efficiency, consolidating it as a significant contribution to the field of cybersecurity. © 2024 IEEE.

关键词： Long short-term memory

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Risk-Averse Learning with Non-Stationary Distributions

arXiv

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arXiv 2024年

作者： Wang, Siyi Wang, Zifan Yi, Xinlei Zavlanos, Michael M. Johansson, Karl H. Hirche, Sandra Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Mechanical Engineering and Material Science Duke University DurhamNC27708 United States

Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is favorable to adopt a strategy that minimizes the negative impact of change to avoid potentially risky situations. In this paper, we investigate risk-averse online optimization where the distribution of the random cost changes over time. We minimize risk-averse objective function using the Conditional Value at Risk (CVaR) as risk measure. Due to the difficulty in obtaining the exact CVaR gradient, we employ a zeroth-order optimization approach that queries the cost function values multiple times at each iteration and estimates the CVaR gradient using the sampled values. To facilitate the regret analysis, we use a variation metric based on Wasserstein distance to capture time-varying distributions. Given that the distribution variation is sub-linear in the total number of episodes, we show that our designed learning algorithm achieves sub-linear dynamic regret with high probability for both convex and strongly convex functions. Moreover, theoretical results suggest that increasing the number of samples leads to a reduction in the dynamic regret bounds until the sampling number reaches a specific limit. Finally, we provide numerical experiments of dynamic pricing in a parking lot to illustrate the efficacy of the designed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

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computer systems science & engineering 2022年第6期41卷 1043-1056页

作者： Xiaorui Zhang Wenfang Zhang Wei Sun Xingming Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszowVoivodeship100031Poland

In a telemedicine diagnosis system,the emergence of 3D imaging enables doctors to make clearer judgments,and its accuracy also directly affects doctors’diagnosis of the *** order to ensure the safe transmission and storage of medical data,a 3D medical watermarking algorithm based on wavelet transform is proposed in this *** proposed algorithm employs the principal component analysis(PCA)transform to reduce the data dimension,which can minimize the error between the extracted components and the original data in the mean square ***,this algorithm helps to create a bacterial foraging model based on particle swarm optimization(BF-PSO),by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding,thereby achieving the optimal balance between embedding capacity and imperceptibility.A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark.

关键词： 3-D medical watermarking robust watermarking PCA BF-PSO

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Fine-Tuning Large Language Models for Syndrome Differentiation in Traditional Chinese Medicine 10th

Fine-Tuning Large Language Models for Syndrome Differentiat...

引用

10th China Health Information Processing Conference, CHIP 2024

作者： Song, Wenlong Li, Zixuan Wang, Huaiyu Yuan, Chi College of Computer Science and Software Engineering Hohai University Nanjing China National Institute of Traditional Chinese Medicine Constitution and Preventive Treatment of Diseases Beijing University of Chinese Medicine Beijing China

ISBN: (纸本)9789819642977

Objective: This study aims to enhance large language models (LLMs) performance on the TCMSD benchmark and strengthen their ability in Traditional Chinese Medicine (TCM) syndrome differentiation. The study explores the use of LLMs to model the complex reasoning processes inherent in TCM diagnosis. Methods: We employed Quantized Low-Rank Adaptation (QLoRA) to fine-tune the Qwen2.5-72B model, specifically tailored to enhance its reasoning ability for TCM syndrome differentiation tasks. Additionally, ensemble learning techniques were utilized to further optimize model performance. Results: Our method achieved a significant improvement on the TCMSD benchmark, reaching a performance score of 31.9050 with the Qwen2.5-72B + QLoRA + Ensemble method. Compared to the baseline Qwen2.5-7B, which scored 24.4804, our method resulted in an improvement of approximately 30.5%. Conclusion: The results suggest that QLoRA fine-tuning, in combination with ensemble learning, can enhance the performance of LLMs in the context of TCM syndrome differentiation. This approach demonstrates the potential of leveraging advanced AI techniques to aid in the science of TCM, offering new opportunities for improving diagnostic accuracy and decision-making in clinical practice. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Feasibility Evaluation of Quadratic Programs for Constrained Control

arXiv

引用

arXiv 2025年

作者： Rousseas, Panagiotis Panagou, Dimitra Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Robotics Department of Aerospace Engineering University of Michigan MI United States

This paper presents a computationally-efficient method for evaluating the feasibility of Quadratic Programs (QPs) for online constrained control. Based on the duality principle, we first show that the feasibility of a QP can be determined by the solution of a properly-defined Linear Program (LP). Our analysis yields a LP that can be solved more efficiently compared to the original QP problem, and more importantly, is simpler in form and can be solved more efficiently compared to existing methods that assess feasibility via LPs. The computational efficiency of the proposed method compared to existing methods for feasibility evaluation is demonstrated in comparative case studies as well as a feasible-constraint selection problem, indicating its promise for online feasibility evaluation of optimization-based controllers. © 2025, CC BY.

关键词： Quadratic programming

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Entirety of Quantum Uncertainty and Its Experimental Verification

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Chinese Physics Letters 2021年第7期38卷 11-16页

作者： Jie Xie Li Zhou Aonan Zhang Huichao Xu Man-Hong Yung Ping Xu Nengkun Yu Lijian Zhang National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences and School of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Centre for Quantum Software and Information School of SoftwareFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNSWAustralia

As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.

关键词： observable union operators

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HIGHER-CRITICISM FOR SPARSE MULTI-STREAM CHANGE-POINT DETECTION

arXiv

引用

arXiv 2024年

作者： Gong, Tingnan Kipnis, Alon Xie, Yao H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States Efi Arazi School of Computer Science Reichman University

We study a statistical procedure based on higher criticism (HC) to address the sparse multi-stream quickest change-point detection problem. Namely, we aim to detect a potential change in the distribution of multiple data streams at some unknown time. If a change occurs, only a few streams are affected, whereas the identity of the affected streams is unknown. The HC-based procedure involves testing for a change point in individual streams and combining multiple tests using higher criticism. Relying on HC thresholding, the procedure also indicates a set of streams suspected to be affected by the change. We provide a theoretical analysis under a sparse heteroscedastic normal change-point model. We establish an information-theoretic detection delay lower bound when individual tests are based on the likelihood ratio or the generalized likelihood ratio statistics and show that the delay of the HC-based method converges in distribution to this bound. In the special case of constant variance, our bound coincides with known results in (Chan, 2017) [6]. We demonstrate the effectiveness of the HC-based method compared to other methods in detecting sparse changes through extensive numerical evaluations. © 2024, CC BY.

关键词： Information theory

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Speech Master: Natural Language Processing and Deep Learning Approach for Automated Speech Evaluation 12

Speech Master: Natural Language Processing and Deep Learning...

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12th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Kooragama, K.G.C.M. Jayashanka, L.R.W.D. Munasinghe, J.A. Jayawardana, K.W. Tissera, Muditha Buddhika, Thilini Sri Lanka Institue of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665400664

Every English speaker wishes to expertise his/her public speaking skills sharply. However, it is extremely difficult and requires a significant amount of practice and experience on an individual basis. This paper introduces a novel online tool 'Speech Master' to practice and improve public English speech delivering skills in a professional manner. Using natural language processing, machine learning, and deep learning approaches, the proposed system analyzes the user's speech in terms of content, grammatical accuracy, grammatical richness, facial expressions, and flow. The accuracy was checked by comparing actual results taken from experts with the predicted results obtained from the tool. 'Speech Master' achieves an average accuracy of more than 80% and produces a better overall result. This novel tool benefits English speakers all over the world by meeting the demand for a simple and easy-to-use solution for improving or practicing English speech delivery skills;enhancing oratory skills, boosting confidence, and delivering well-articulated speeches. © 2021 IEEE.

关键词： Sentiment analysis

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