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arXiv 2024年

作者： Wang, Guan-Ren Tsai, Chung-En Cheng, Hao-Chung Li, Yen-Huan Graduate Institute of Networking and Multimedia National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Electrical Engineering Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Mathematics National Taiwan University Taiwan Quantum Computing Centre Taiwan Center for Quantum Science and Engineering National Taiwan University Taiwan

We propose a Riemannian gradient descent with the Poincaré metric to compute the order-α Augustin information, a widely used quantity for characterizing exponential error behaviors in information theory. We prove that the algorithm converges to the optimum at a rate of O(1/T). As far as we know, this is the first algorithm with a non-asymptotic optimization error guarantee for all positive orders. Numerical experimental results demonstrate the empirical efficiency of the algorithm. Our result is based on a novel hybrid analysis of Riemannian gradient descent for functions that are geodesically convex in a Riemannian metric and geodesically smooth in another. Copyright © 2024, The Authors. All rights reserved.

关键词： Information theory

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Fixed-time rotating consensus control for multi-agent systems

Fixed-time rotating consensus control for multi-agent system...

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Chinese Control and Decision Conference, CCDC

作者： Chunchun Cheng Yi Huang Pengfei Xu Long Jian Guangyu Zuo Liwei Kou Department of Basic Sciences Shanxi Agricultural University Jinzhong China School of Information and Electrical Engineering Hunan University of Science and Technology Changsha China School of Mathematics and Computer Science Shanxi Normal University Taiyuan China College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Energy Internet Research Institute Taiyuan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper investigates the fixed-time rotating consensus (FRC) problem for the leaderless multi-agent systems subject to the external time-varying disturbances. The communication topology among agents is described by a strongly connected directed graph. Firstly, a distributed controller is proposed to drive the agents such that they achieve the rotating consensus within a fixed time irrelevant to the initial conditions. An integral sliding mode control technique is then utilized to compensate the time-varying disturbances. Finally, simulation results are given to illustrate the performance of the proposed controller.

关键词： Simulation Directed graphs Drives Consensus control Numerical simulation Topology Time-varying systems

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities

The Reliability of Information Sources: Student Perceptions ...

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International IEEE Conference and Workshop: Óbuda on electrical and Power engineering (CANDO-EPE)

作者： Anita Mihalovicsne Kollar Jozsef Katona Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources; social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape.

关键词： Economics Power engineering Accuracy Social networking (online) engineering profession Education Reliability engineering Information filters Information age Reliability

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A Hybrid Residual CNN with Channel Attention Mechanism for Continuous User Identification Using Wearable Motion Sensors

A Hybrid Residual CNN with Channel Attention Mechanism for C...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Recognizing individuals continuously using wearable devices has several applications in personalized services and security. This paper presents a hybrid deep network architecture called SE-ResBiLSTM that integrates a squeeze-and-excitation (SE) mechanism with residual bidirectional LSTM layers. The goal is to enhance the accuracy of person identification using data from wearable motion sensors. The SE blocks facilitate channel-wise feature refinement, highlighting helpful information. Residual connections enable the training of deeper LSTM architectures. The proposed method's accuracy in identifying individuals across increasing periods between training and testing is evaluated using the SP-SW HAR dataset, which contains activity data collected from wristwatch sensors. By leveraging representation learning and exhibiting resilience against sensor noise and temporal fluctuations, SE-ResBiLSTM improves recognition accuracy compared to regular and convolutional LSTM networks. According to the trial data, SE-ResBiLSTM achieved a maximum identification accuracy of 98.71 %. The experimental results demonstrate that this approach significantly boosts the performance of user identification using wristwatch sensors.

关键词： Training Representation learning Accuracy Motion detection Telecommunications Security Wearable devices

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Securing Smart Homes Using Hybrid Stacking Ensemble Deep Learning-Enabled Framework

Securing Smart Homes Using Hybrid Stacking Ensemble Deep Lea...

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electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Abdallah Moubayed MohammadNoor Injadat Tamer Mohamed Abdellatif Mohamed Sattam Almatarneh Malak Al-Mashagbeh Mohammad Aljaidi School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Electrical & Computer Engineering Department Lebanese American University Byblos Lebanon Electrical & Computer Engineering Department Western University London Ontario Canada Data Science & Artificial Intelligence Department Zarqa University Zarqa Jordan School of Engineering Applied Science and Technology Canadian University Dubai Dubai UAE Cyber Security Department Zarqa University Zarqa Jordan Computer Science Department Zarqa University Zarqa Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The added deployment of Internet-of-Things (IoT) devices in various environments has led to people increasingly adding this technology to their homes to make them more comfortable and have a more convenient living environment. Despite the promise and the growth potential of the smart home market, one main concern is the security and privacy of smart home devices. This is because smart home devices equipped with IoT technology are prone to various network-based attacks. With smart home devices having limited computation and memory capabilities, many IoT manufacturers often overlook the security aspect by focusing more on the functionality. To address this issue, the concepts of machine learning (ML) and deep learning (DL) have emerged as viable and promising solutions. To that end, this paper proposes a hybrid stacking ensemble DL-enabled framework that combines both classical ML models with sophisticated DL models. The goal is to make use of the simplicity and interpretability of the classical ML models with the superior time-dependent patterns that DL models can detect. The proposed framework's performance is evaluated using multiple datasets representing different smart home devices. Experimental results show that the proposed framework achieved better detection accuracy, precision, recall, and F-score.

关键词： Performance evaluation Deep learning Privacy Accuracy Stacking Focusing Smart homes

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ITERATIVE SINGULAR TUBE HARD THRESHOLDING ALGORITHMS FOR TENSOR RECOVERY

arXiv

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arXiv 2023年

作者： Grotheer, Rachel Li, Shuang Ma, Anna Needell, Deanna Qin, Jing Department of Mathematics Wofford College SpartanburgSC29303 United States Department of Electrical and Computer Engineering Iowa State University AmesIA50011 United States Department of Mathematics University of California IrvineCA92697 United States Department of Mathematics University of California Los AngelesCA90095 United States Department of Mathematics University of Kentucky LexingtonKY40506 United States

Due to the explosive growth of large-scale data sets, tensors have been a vital tool to analyze and process high-dimensional data. Different from the matrix case, tensor decomposition has been defined in various formats, which can be further used to define the best low-rank approximation of a tensor to significantly reduce the dimensionality for signal compression and recovery. In this paper, we consider the low-rank tensor recovery problem when the tubal rank of the underlying tensor is given or estimated a priori. We propose a novel class of iterative singular tube hard thresholding algorithms for tensor recovery based on the low-tubal-rank tensor approximation, including basic, accelerated deterministic and stochastic versions. Convergence guarantees are provided along with the special case when the measurements are linear. Numerical experiments on tensor compressive sensing and color image inpainting are conducted to demonstrate convergence and computational efficiency in practice. Copyright © 2023, The Authors. All rights reserved.

关键词： Tensors

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GaN/SiC Upper F-Band Single Ended Power Amplifier

GaN/SiC Upper F-Band Single Ended Power Amplifier

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Microwave and Antenna Symposium (IMAS), International

作者： German Cortes Kevin T. Kornegay Willie L. Thompson II Michel Kornegay Ali Darwish ESI Directorate US Army DEVCOM C5ISR Center Aberdeen Proving Ground MD United States Department of Electrical and Computer Engineering Morgan State University Baltimore MD United States Air Missile Defense Sector Applied Physics Laboratory John Hopkins University Laurel MD United States Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD United States

ISBN: (数字)9798350356359

ISBN: (纸本)9798350356366

This article presents the design implementation, analysis and performance of a four-stage single ended power amplifier (PA) developed on a 40 nm gallium nitride on silicon carbide (GaN/SiC) technology. The circuit targets the upper F-Band, and it operates from 125 GHz to 130 GHz. At 130 GHz, the amplifier aims to have a small signal power gain $G$ p of 18 dB, peak power-added efficiency (PAE) of 10, and to achieve a saturated output power (PSat) of 27 dBm. Based on EM simulations this is the highest power level reported on this technology at the frequency of operation to date.

关键词： Microwave antennas Isolators Power measurement Silicon carbide Power amplifiers Integrated circuit modeling Probes Gallium nitride Gain Power generation

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A Generalization of Multi-Source Fusion-Based Framework to Stock Selection

SSRN

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SSRN 2023年

作者： Snášel, Václav Velasquez Silva, Juan Domingo Pant, Millie Georgiou, Dimitrios Kong, Lingping Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Czech Republic Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India Departament of Industrial Engineering University of Chile Santiago Chile Faculty of Electrical Engineering and Computer Science National Technical University of Athens Greece Chile Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India

Selecting outstanding tech stocks for investment is challenging. Specifically, studying the investment for academic purposes is not mature enough due to the disarray of the publications and the `over' informative experience from profit-making websites. Often some authors entitle stock price prediction subject as a stock selection problem;both prediction and selection are just sub-section of portfolio management. Moreover, stock websites provide tons of potential criteria showing their perspective evaluation to simulate and monitor the stock market professionally, which causes an extra problem for academic studies screening out the less informative criteria. The paper generalizes a novel framework with a user-interactive interface for stock selection problems based on multi-source data fusion and decision-level fusion to enhance reliability that is limited by the narrow criteria performance and to enhance the strength of a model overcoming the weakness of a single-performed model. This framework benefits the time-series prediction and decision-making study. Besides, we propose adopting dynamic time warping to assist a task-learning process by customizing a loss function that improves the accuracy of data prediction. The experiment shows that the proposed method reduces the prediction log error by 28\% in a best case of real-situation data and decreases the warping cost by 20\%. Finally, we illustrate the proposed framework by implementing it in a real-world stock data selection. The results are practical and effective, further justified through a detailed ablation study. © 2023, The Authors. All rights reserved.

关键词： Investments

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Relationship between Uncertainty in DNNs and Adversarial Attacks

arXiv

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arXiv 2024年

作者： Ogonna, Mabel Adeniran, Abigail Adeyemo, Adewale Rawls College of Business Texas Tech University LubbockTX79409 United States Department of Computing Science and Mathematics University of Stirling Scotland United Kingdom Department of Electrical and Computer Engineering Tennessee Technological University CookevilleTN38505 United States

Deep Neural Networks (DNNs) have achieved stateofthe-art results and even outperformed human accuracy in many challenging tasks, leading to DNNs’ adoption in a variety of fields including natural language processing, pattern recognition, prediction, and control optimization. However, DNNs are accompanied by uncertainty about their results, causing them to predict an outcome that is either incorrect or outside of a certain level of confidence. These uncertainties stem from model or data constraints, which could be exacerbated by adversarial attacks. Adversarial attacks aim to provide perturbed input to DNNs, causing the DNN to make incorrect predictions or increase model uncertainty. In this review, we explore the relationship between DNN uncertainty and adversarial attacks, emphasizing how adversarial attacks might raise DNN uncertainty. © 2024, CC BY.

关键词： Generative adversarial networks

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DDPS: Dynamic Differential Pricing-based Edge Offloading System with Energy Harvesting Devices

arXiv

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arXiv 2024年

作者： Xue, Hai Xia, Yun Xiong, Neal N. Zhang, Di Pei, Songwen School of Optical- Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States School of Electrical and Information Engineering Zhengzhou University the Henan International Joint Laboratory of Intelligent Health Information System the National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China

Mobile edge computing (MEC) paves the way to alleviate the burden of energy and computation of mobile users (MUs) by offloading tasks to the network edge. To enhance the MEC server utilization by optimizing its resource allocation, a well-designed pricing strategy is indispensable. In this paper, we consider the edge offloading scenario with energy harvesting devices, and propose a dynamic differential pricing system (DDPS), which determines the price per unit time according to the usage of computing resources to improve the edge server utilization. Firstly, we propose an offloading decision algorithm to decide whether to conduct the offloading operation and how much data to be offloaded if conducted, the algorithm determines offloading operation by balancing the energy harvested with the energy consumed. Secondly, for the offloading case, we formulate the game between the MUs and the server as a Stackelberg game, and propose a differential pricing algorithm to determine the optimal computing resources required by MUs. Furthermore, the proposed algorithm also reallocates computing resources for delay-sensitive devices while server resources are surplus after the initial allocation, aiming to make full use of the server computing resources. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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