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12th International Symposium on Topics in Coding, ISTC 2023

作者： Rengaswamy, Narayanan Raina, Ankur Raveendran, Nithin Vasic, Bane University of Arizona Tucson Department of Electrical and Computer Engineering Arizona85721 United States Indian Institute of Science Education and Research Department of Electrical Engineering and Computer Science Madhya Pradesh Bhopal462066 India

ISBN: (纸本)9798350326116

Entanglement distribution is a critical task in quantum networks. Since the distributed entanglement can suffer from noise in the channel, it is necessary to develop methods that distill higher quality entanglement from the shared noisy entangled states. In this work, we propose a protocol to distill multi-qubit Greenberger-Horne-Zeilinger (GHZ) states among the nodes of a network using quantum error correcting codes. The method builds upon a Bell state distillation protocol by Wilde et at. (2007) that we recently generalized to 3-qubit GHZ states. The key technical result that enables our protocol shows how measuring a Pauli operator, or in general a set of code stabilizers, on one subsystem of a multipartite GHZ state affects the other subsystems. The design and analysis of the protocol is driven by the stabilizer formalism for measurements, and we provide discussions to elucidate the steps of the protocol. A similar approach can be applied to distill other multipartite entangled states as long as they are stabilizer states. © 2023 IEEE.

关键词： Quantum entanglement

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Transferable Learning of GCN Sampling Graph Data Clusters from Different Power systems 60

Transferable Learning of GCN Sampling Graph Data Clusters fr...

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Wu, Tong Scaglione, Anna Arnold, Daniel Chen, Tianyi University of Central Florida The Department of Electrical and Computer Engi-neering OrlandoFL32816 United States Cornell University The Department of Electrical and Computer Engineering Cornell Tech New York CityNY10044 United States Lawrence Berkeley National Laboratory Rensselaer Polytechnic Institute The Department of Electrical TroyNY12180 United States Computer and Systems Engineering Rensselaer Polytechnic Institute The Department of Electrical TroyNY12180 United States

ISBN: (纸本)9798331541033

Contemporary neural network (NN) detectors for power systems face two primary challenges. First, each power system requires individual training of NN detectors to accommodate its unique configuration and base demands. Second, significant changes within the power system, such as the introduction of new substations or new generators, necessitate retraining. To overcome these issues, we introduce a novel architecture, the Nodal Graph Convolutional Neural Network (NGCN), which utilizes graph convolutions at each bus and its neighborhoods. This approach allows the training process to encompass multiple power systems and include all buses, thereby enhancing the transferability of the method across different power systems. The NGCN is particularly effective for detection tasks, such as cyber-attacks on smart inverters and false data injection attacks. Our tests demonstrate that the NGCN significantly improves performance over traditional NNs, boosting detection accuracy from approximately 85% to around 97% for the aforementioned task. Furthermore, the transferable NGCN, which is trained by samples from multiple power systems, performs considerably better in evaluations than the NGCN trained on a single power system. © 2024 IEEE.

关键词： Convolutional neural networks

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Integrating Variable Reduction Strategy With Evolutionary Algorithms for Solving Nonlinear Equations systems

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 75-89页

作者： Aijuan Song Guohua Wu Witold Pedrycz Ling Wang School of Traffic and Transportation Engineering Central South UniversityChangsha 410075China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2V4Canada Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Systems Research Institute Polish Academy of SciencesWarsaw 01447Poland Department of Automation Tsinghua UniversityBeijing 100084China

Nonlinear equations systems(NESs)are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple *** algorithms(EAs)are one of the methods for solving NESs,given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one ***,the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used *** contrast,problem domain knowledge of NESs is investigated in this study,where we propose the incorporation of a variable reduction strategy(VRS)into EAs to solve *** VRS makes full use of the systems of expressing a NES and uses some variables(i.e.,core variable)to represent other variables(i.e.,reduced variables)through variable relationships that exist in the equation *** enables the reduction of partial variables and equations and shrinks the decision space,thereby reducing the complexity of the problem and improving the search efficiency of the *** test the effectiveness of VRS in dealing with NESs,this paper mainly integrates the VRS into two existing state-of-the-art EA methods(i.e.,MONES and DR-JADE)according to the integration framework of the VRS and EA,*** results show that,with the assistance of the VRS,the EA methods can produce better results than the original methods and other compared ***,extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.

关键词： Evolutionary algorithm(EA) nonlinear equations systems(ENSs) problem domain knowledge variable reduction strategy(VRS)

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Security and Privacy Challenges in SDN-Enabled IoT systems: Causes, Proposed Solutions,and Future Directions

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computers, Materials & Continua 2024年第8期80卷 2511-2533页

作者： Ahmad Rahdari Ahmad Jalili Mehdi Esnaashari Mehdi Gheisari Alisa A.Vorobeva Zhaoxi Fang Panjun Sun Viktoriia M.Korzhuk Ilya Popov Zongda Wu Hamid Tahaei Institute of Artificial Intelligence Shaoxing UniversityShaoxing312000China Department of Computer Engineering Gonbad Kavous UniversityGonbad-e Kavus49717-99151Iran Faculty of Computer Engineering K.N.Toosi University of TechnologyTehran196976-4499Iran Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Secure Information Technologies Department National Research University ITMOSt.Petersburg197101Russia School of Electrical and Computer Engineering Shiraz UniversityShiraz71946–84334Iran Department of Computer Science and Engineering Islamic Azad UniversityDamavand***Iran Department of R&D Shenzhen BKD Co.Ltd.Shenzhen518000China

Software-Defined Networking(SDN)represents a significant paradigm shift in network architecture,separating network logic from the underlying forwarding devices to enhance flexibility and centralize ***-rently,the Internet of Things(IoT)connects numerous devices to the Internet,enabling autonomous interactions with minimal human ***,implementing and managing an SDN-IoT system is inherently complex,particularly for those with limited resources,as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN *** findings of this study underscore the primary security and privacy challenges across application,control,and data planes.A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy *** investigations have explored cutting-edge methods,such as leveraging blockchain for transaction recording to enhance security and privacy,along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service(DoS)and Distributed DoS(DDoS)***,the analysis indicates that encryption and hashing techniques are prevalent in the data plane,whereas access control and certificate authorization are prominently considered in the control plane,and authentication is commonly employed within the application ***,this paper outlines future directions,offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem.

关键词： Security privacy-preserving software-defined network internet of things

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Classification of Stress using Machine Learning Based on Physiological and Psychological Data from Wearables 15

Classification of Stress using Machine Learning Based on Phy...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Pauzi, Tuan Muhamad Affiq Aimullah Tuan Mohd Samah, Asma Abu Dela Cruz, Jennifer C. Ghaffa, Dalilah Nordin, Rosdiadee Abdullah, Nor Fadzilah Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Electrical Electronic and Systems Engineering Bangi Malaysia Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Wireless Research@UKM Department of Electrical Electronic and Systems Engineering Bangi Malaysia School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines

ISBN: (纸本)9798350381177

Stress is a common psychological issue in today's world, causing individuals to experience physiological and behavioral changes. When someone experiences stress for a prolonged period, it can have an impact on their body and thoughts, leading to severe behaviors. Stress-related problems can be prevented if identified early. Nowadays, researchers' use of machine learning in early disease detection is increasing, offering a solution to stress-related issues. This study focuses on developing a machine learning predictive model for classifying stress levels using physiological data. Physiological data is obtained from an open-source dataset containing biosignal data such as Electrodermal (EDA), Skin Temperature, Heart Rate (HR), Blood Volume Pulse (BVP), and others. The dataset is then used to train a machine learning model using Support Vector Machine (SVM), Random Forest (RF), Adaboost, and Bootstrapping algorithms. Accuracy, precision, recall, and F1-score are used to evaluate the model's performance. As a result, the predictive model using the Bootstrapping algorithm achieves the best performance with an accuracy of 95.82%, a precision of 99.33%, a recall of 98.02%, and F1-score of 94.69% compared to other machine learning classifiers. Creating such models can aid in early stress detection once they are integrated into a wearable device for continuous monitoring purposes. © 2023 IEEE.

关键词： Decision trees

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Frequency-domain analysis of fluid-structure interaction in aircraft hydraulic pipeline systems: numerical and experimental studies

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Journal of Zhejiang University-Science A(Applied Physics & engineering) 2024年第8期25卷 605-617页

作者： Yang DENG Zongxia JIAO Yuanzhi XU School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Research Institute for Frontier Science Beihang UniversityBeijing 100191China Key Laboratory of Advanced Airborne Systems Beihang UniversityBeijing 100191China Ningbo Institute of Technology Beihang UniversityNingbo 315800China Tianmushan Laboratory Hangzhou 310023China

The fluid-structure interaction(FSI)in aircraft hydraulic pipeline systems is of great concern because of the damage it *** accurately predict the vibration characteristic of long hydraulic pipelines with curved segments,we studied the frequency-domain modeling and solution method for FSI in these pipeline *** partial differential equations(PDEs)are utilized to model the pipeline FSI,considering both frequency-dependent friction and bending-flexibility *** address the numerical instability encountered by the traditional transfer matrix method(TMM)in solving relatively complex pipelines,an improved TMM is proposed for solving the PDEs in the frequency domain,based on the matrix-stacking strategy and matrix representation of boundary *** proposed FSI model and improved solution method are validated by numerical cases and *** experimental rig of a practical hydraulic system,consisting of an aircraft engine-driven pump,a Z-shaped aero-hydraulic pipeline,and a throttle valve,was constructed for *** magnitude ratio of acceleration to pressure is introduced to evaluate the theoretical and experimental results,which indicate that the proposed model and solution method are effective in practical *** methodology presented in this paper can be used as an efficient approach for the vibrational design of aircraft hydraulic pipeline systems.

关键词： Fluid-structure interaction(FSI) Frequency-domain analysis Aircraft hydraulic pipeline Pipeline vibration Transfer matrix method(TMM)

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Meta Learning for Domain Agnostic Soft Prompt 48

Meta Learning for Domain Agnostic Soft Prompt

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chen, Ming-Yen Rohmatillah, Mahdin Lee, Ching-Hsien Chien, Jen-Tzung National Yang Ming Chiao Tung University Institute of Electrical and Computer Engineering Taiwan Industrial Technology Research Institute Information and Communications Research Labs Taiwan

ISBN: (纸本)9781728163277

The prompt-based learning, as used in GPT-3, has become a popular approach to extract knowledge from a powerful pre-trained language model (PLM) for natural language understanding tasks. However, either applying the hard prompt for sentences by defining a collection of human-engineering prompt templates or directly optimizing the soft or continuous prompt with labeled data may not really generalize well for unseen domain data. To cope with this issue, this paper presents a new prompt-based unsupervised domain adaptation where the learned soft prompt is able to boost the frozen pre-trained language model to deal with the input tokens from unseen domains. Importantly, the meta learning and optimization is developed to carry out the domain agnostic soft prompt where the loss for masked language model is minimized. The experiments on multi-domain natural language understanding tasks show the merits of the proposed method. © 2023 IEEE.

关键词： Domain adaptation meta-learning natural language understanding prompt-based learning

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Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

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computer systems Science & engineering 2022年第10期43卷 275-289页

作者： V.Ulagamuthalvi G.Kulanthaivel A.Balasundaram Arun Kumar Sivaraman Department of Computer Science and Engineering Sathyabama UniversityChennai600119India Department of Electrical Electronics and Communication EngineeringNITTTRChennai600113India Centre for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India

One of the fast-growing disease affecting women’s health seriously is breast *** is highly essential to identify and detect breast cancer in the earlier *** paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer *** learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical *** methods focused on using various conventional and machine learning methods for processing natural and medical *** is inadequate for the image where the coarse structure matters *** of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in *** deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate *** this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram *** performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

关键词： Medical image processing deep learning convolution neural network breast cancer feature extraction classification

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Periodic poling of single crystal thin film barium titanate-on-insulator

Periodic poling of single crystal thin film barium titanate-...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Aashna, Pragati Lin, Hong-Lin Cao, Yu Gao, Yuan Mohanraj, Sakthi Sanjeev Zhu, Di Danner, Aaron Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore Department of Material Science and Engineering National University of Singapore 117575 Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research 138634 Singapore

We report, for the first time, electric field periodic poling of single-crystal thin film barium titanate grown using pulsed-laser-deposition on dysprosium scandate substrate. Uniform domains with periods 5-7 μm are ... 详细信息

关键词： Single crystals

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Enhancing Data Security and Privacy in Cloud Computing: A Survey of Modern Techniques 3

Enhancing Data Security and Privacy in Cloud Computing: A Su...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Suganya, N. Gouthami, P. Sathiya, R. Sivaranjani, S. Murugesan, M. Department of Computer Science and Business Systems Dr. N.G.P. Institute of Technology Coimbatore India Department of Information Technology Thangavelu Engineering College Chennai India Departement of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Departement of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798331527549

A computing system that is based on the internet and offers users all resources as on-demand services. Servers, storage, databases, software, and networking are among these on-demand services. Usually, the user must pay for the use of the resources;they are not their property. The number of people using this technology is increasing dramatically as a result of these features. Although it has benefits, cloud computing poses certain security and privacy risks. The authorized user’s data can be stolen easily. This current research thoroughly investigates modern strategies to these problems, highlighting identity management protocols, effective encryption and blockchain-based solutions. The article identifies research gaps, focuses attention to the inadequacies of current approaches, and recommends the possible ways to improve the efficiency, scalability and trustworthiness. © 2025 IEEE.

关键词： Cloud security

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