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Frontiers in Education (FIE) Conference

作者： Sujing Wang Qiang Xu Stefan Andrei Department of Computer Science Lamar University Beaumont TX USA Dan F. Smith Department of Chemical and Biomolecular Engineering Lamar University Beaumont TX USA Computer Systems Engineering Technology Department Oregon Institute of Technology Klamath Falls OR USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice full paper introduces the design, organization, and evaluation of two one-week summer camps to introduce multi-disciplinary engineering technology and programming to high school students. This program is designed to increase students' interests and knowledge of multi-disciplinary engineering and computing, foster collaboration, and build community. By engaging in our program, students not only think critically and enhance their problem-solving skills but also develop foundational proficiency in engineering and computational thinking. This crucial skill set is recognized as essential in the 21st century, opening doors to enriching engineering capabilities and creating diverse opportunities for pursuing careers in multiple engineering fields. This paper introduces the organizational structure, coordination strategies, core curriculum instructions, and the program assessment result which demonstrates a substantial improvement in knowledge and increased interest in computing and engineering across all participant cohorts. The primary objective of this paper is to furnish a detailed guide, equipping institutions with the essential information required to successfully host similar summer programs. Furthermore, it aims to catalyze endeavors geared towards enhancing young students' participation in the fields of computing and engineering.

关键词： Knowledge engineering engineering profession Collaboration Organizations Problem-solving Programming profession

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Learning Activation Functions for Sparse Neural Networks

arXiv

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arXiv 2023年

作者： Loni, Mohammad Mohan, Aditya Asadi, Mehdi Lindauer, Marius Division of Computer Science and Software Engineering Mälardalen University Sweden Institute of Artificial Intelligence Leibniz University Hannover Germany Department of Electrical Engineering Tarbiat Modares University Tehran Iran

Sparse Neural Networks (SNNs) can potentially demonstrate similar performance to their dense counterparts while saving significant energy and memory at inference. However, the accuracy drop incurred by SNNs, especially at high pruning ratios, can be an issue in critical deployment conditions. While recent works mitigate this issue through sophisticated pruning techniques, we shift our focus to an overlooked factor: hyperparameters and activation functions. Our analyses have shown that the accuracy drop can additionally be attributed to (i) Using ReLU as the default choice for activation functions unanimously, and (ii) Fine-tuning SNNs with the same hyperparameters as dense counterparts. Thus, we focus on learning a novel way to tune activation functions for sparse networks and combining these with a separate hyperparameter optimization (HPO) regime for sparse networks. By conducting experiments on popular DNN models (LeNet-5, VGG-16, ResNet-18, and EfficientNet-B0) trained on MNIST, CIFAR-10, and ImageNet-16 datasets, we show that the novel combination of these two approaches, dubbed Sparse Activation Function Search, short: SAFS, results in up to 15.53%, 8.88%, and 6.33% absolute improvement in the accuracy for LeNet-5, VGG-16, and ResNet-18 over the default training protocols, especially at high pruning ratios.1 © 2023, CC BY.

关键词： Drops

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Identity Based Encryption and Biometric Authentication Scheme for Secure Data Access in Cloud Computing

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Chinese Journal of Electronics 2023年第2期21卷 254-259页

作者： Hongbing Cheng Chunming Rong Zhenghua Tan Qingkai Zeng State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Information Engineering Jiangsu City Vocation College Nanjing China Department of Electronic Engineering and Computer Science University of Stavanger Stavanger Norway Department of Electronic Systems Aalborg University Aalborg Denmark

Cloud computing will be a main information infrastructure in the future; it consists of many large datacenters which are usually geographically distributed and heterogeneous. How to design a secure data access for cloud computing platform is a big challenge. In this paper, we propose a secure data access scheme based on identity-based encryption and biometric authentication for cloud computing. Firstly, we describe the security concern of cloud computing and then propose an integrated data access scheme for cloud computing, the procedure of the proposed scheme include parameter setup, key distribution, feature template creation, cloud data processing and secure data access control. Finally, we compare the proposed scheme with other schemes through comprehensive analysis and simulation. The results show that the proposed data access scheme is feasible and secure for cloud computing.

关键词： Access control Cloud computing Identity-based encryption Biometrics (access control) Authentication Process control Memory

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Disjoint Compatibility via graph classes

arXiv

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arXiv 2024年

作者： Aichholzer, Oswin Obmann, Julia Paták, Pavel Perz, Daniel Tkadlec, Josef Vogtenhuber, Birgit Graz University of Technology Institute of Software Technology Austria Czech Technical University in Prague Department of Applied Mathematics Czech Republic University of Perugia Department of Engineering Italy Charles University Prague Computer Science Institute Czech Republic

Two plane drawings of graphs on the same set of points are called disjoint compatible if their union is plane and they do not have an edge in common. Let S be a convex point set of 2n ≥ 10 points and let H be a family of plane drawings on S. Two plane perfect matchings M1 and M2 on S (which do not need to be disjoint nor compatible) are disjoint H-compatible if there exists a drawing in H which is disjoint compatible to both M1 and M2. In this work, we consider the graph which has all plane perfect matchings as vertices and where two vertices are connected by an edge if the matchings are disjoint H-compatible. We study the diameter of this graph when H is the family of all plane spanning trees, caterpillars or paths. We show that in the first two cases the graph is connected with constant and linear diameter, respectively, while in the third case it is disconnected. © 2024, CC BY-NC-ND.

关键词： Graphic methods

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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MEA-Defender: A Robust Watermark against Model Extraction Attack

MEA-Defender: A Robust Watermark against Model Extraction At...

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IEEE Symposium on Security and Privacy

作者： Peizhuo Lv Hualong Ma Kai Chen Jiachen Zhou Shengzhi Zhang Ruigang Liang Shenchen Zhu Pan Li Yingjun Zhang Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Department of Computer Science Metropolitan College Boston University USA Institute of Software Chinese Academy of Sciences China

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Recently, numerous highly-valuable Deep Neural Networks (DNNs) have been trained using deep learning algorithms. To protect the Intellectual Property (IP) of the original owners over such DNN models, backdoor-based watermarks have been extensively studied. However, most of such watermarks fail upon model extraction attack, which utilizes input samples to query the target model and obtains the corresponding outputs, thus training a substitute model using such input-output pairs. In this paper, we propose a novel watermark to protect IP of DNN models against model extraction, named MEA-Defender. In particular, we obtain the watermark by combining two samples from two source classes in the input domain and design a watermark loss function that makes the output domain of the watermark within that of the main task samples. Since both the input domain and the output domain of our watermark are indispensable parts of those of the main task samples, the watermark will be extracted into the stolen model along with the main task during model extraction. We conduct extensive experiments on four model extraction attacks, using five datasets and six models trained based on supervised learning and self-supervised learning algorithms. The experimental results demonstrate that MEA-Defender is highly robust against different model extraction attacks, and various watermark removal/detection approaches.

关键词： Training Symbiosis Computational modeling Supervised learning Watermarking Artificial neural networks Speech recognition

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Optimizing Load and Routes in Supply Chain Management with Travelling Salesman Problem and CapsNet Attention 3

Optimizing Load and Routes in Supply Chain Management with T...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Rajasekar, P. Begam, M. Shabeena Chattopadhyay, Shouvik Basha, S. Chand Chandre, Shanker Dwivedi, Vijay Kumar Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India Sns College of Technology Department of Mba Coimbatore India Institute of Engineering & Management Department of Mba Kolkata India University of Engineering & Management Kolkata India St Ann's College of Engineering and Technology Department of Business Administration Chirala India School of Computer Science & Artificial Intelligence Sr University Department of Computer Science & Artificial Intelligence Warangal India Vishwavidyalaya Engineering College Department of Mathematics Ambikapur India

ISBN: (纸本)9798331508456

Ever since the industrial revolution, industries have partnered to solve marketing problems and create new channels. Because they don't understand their clients' needs, sales centers often pursue conflicting aims. Lack of information has extended delivery times. Due to supply time, quality, or bulk freight depreciation agreements, facility expenses rise to satisfy demand, resulting in excess inventory. Supply chain management, particularly regional inventory management, has considerably impacted beer manufacturing profitability. The recommended method involves training the model and extracting features. Dynamic and PCA feature extraction are different. While PCA's main purpose is to dissociate correlated from uncorrelated data, dynamic PCA considers auto- and cross-correlation in time-series observations. A revised CapsNet approach trained the models. The Improved CapsNet model outperformed the Attention and baseline models in every test. The model solved brewing supply chain issues with 94.68% accuracy. The most successful advanced machine learning approaches for improving SCM include augmented CapsNet, PCA, and dynamic PCA, according to research. These techniques boost industrial profitability when applied to major issues like excess stock and supply unpredictability. The suggested strategy is more reliable and superior than present solutions, according to results. © 2025 IEEE.

关键词： Supply chain management

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Thematic Event Extraction Based on Event-Related Sentence Detection

Thematic Event Extraction Based on Event-Related Sentence De...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xing Zhang Yucan Guo Xiaolong Jin Haozhe Liang Saiping Guan School of Computer Science and Technology University of Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute of Systems Engineering Academy of Military Science

Existing works in event extraction typically extract event arguments within the sentence scope. However, besides the sentence level, events may also be naturally presented at the document level. A document-level event usually reflects, to some extent, the theme (i.e., the main content) of the document (e.g., electronic medical records and news articles), which is thus referred to as the thematic event. Thematic Event Extraction (TEE) aims to extract the arguments of thematic events. TEE faces a major challenge, i.e., the sparsity and dispersion of arguments, which means that the arguments of a thematic event are dispersed in different sentences of the document. To overcome this challenge, we propose an Event-related Sentence Detection based TEE model, called ESDTEE, which first detects the sentences related to the thematic event and then extracts the arguments only within these detected sentences using existing models. Extensive experiments with comprehensive analyses demonstrate the effectiveness of ESDTEE.

关键词：

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Feature Points based Residual UNet with Nonlinear Decay Rate for Partial Wet Fingerprint Restoration and Recognition

Feature Points based Residual UNet with Nonlinear Decay Rate...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： An-Ting Hsieh Ching-Te Chiu Tsai-Chieh Chen Mao-Hsiu Hsu Long Wenyong Institute of Information Systems and Applications National Tsing Hua University Department of Computer Science National Tsing Hua University Department of Electro-Optical Engineering National Formosa University FocalTech Systems Co. Ltd.

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

This paper proposes FPN-ResUNet, which uses feature points-based restoration and nonlinear decay rate residual within a U-shape architecture to restore blurry and wet fingerprints within size constraints effectively. Our approach aims to restore fingerprints effectively while avoiding over-restoration and preserving local matching feature points, which reduces the false rejection rate (FRR). We develop novel residual blocks, which feature a nonlinear decay mechanism that optimizes weight allocation between blocks to enhance feature extraction capabilities. Furthermore, our residual feature points fusion module restores contextual information by fusing matching feature points and features from the previous *** comprehensive experiments on real-world data, we achieved a remarkable 9.4% reduction in FRR. Our method outperforms the basic U-Net framework [1] by 50.52% in overall performance. We improved 48.35% over FPDMNet [2] and 58.77% over DenseUNet [3] which is trained with small patches. Moreover, PGT-Net [4] is also used for small areas of wet fingerprints, but our FPNResUNet outperformed it by 72.51%.

关键词： Image recognition Accuracy Circuits and systems Image matching Fingerprint recognition Feature extraction Image restoration

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Probabilistic Health Monitoring and Maintenance Scheduling for Nuclear Power systems

Probabilistic Health Monitoring and Maintenance Scheduling f...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Amer, Ahmad Tsilifis, Panagiotis Saxena, Abhinav Baglietto, Emilio Khan, Genghis Probabilistic Design & Material Informatics GE Aerospace Research Niskayuna NY12309 United States Robotics AI & Computer Vision GE Vernova Advanced Research Niskayuna NY12309 United States AI & Computer Vision GE Aerospace Research San RamonCA94583 United States Department of Nuclear Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Sustainable Systems GE Aerospace Research Niskayuna NY12309 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Operations and Maintenance (O&M) cost optimization in the nuclear energy industry is an imperative task for developing sustainable systems and efficient renewable technologies. We present a modular probabilistic framework for addressing maintenance scheduling challenges for small modular nuclear reactors using a Dynamic Bayesian Network (DBN) approach that monitors fatigue and performance in multiple subsystems of the reactors and provides a probabilistic assessment for potential failure events. The DBN framework fuses information from potential digital twins that assimilate real time data from several sensors with different time granularities in order to monitor and update thecurrentstate of knowledge aboutthestatus of several fatigue or performance criteria. Comprised of multi-modal digital twins (empirical, data- and physics-based twins), this DBN serves as a predictive framework forecasting the future state of specific reactor subsystems and enhancing decision-making capabilities associated with preventing system failure and minimizing the time and cost of maintenance events. The work presented in this paper is a holistic approach for monitoring and safeguarding complex, multi-component engineering systems where maintenance scheduling is critical and does not restrict itself to nuclear environments. However, our goal is to illustrate how such a framework can be adjusted to small modular water reactors. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Nuclear energy

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