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Pervasive and Intelligent Computing (PICom), IEEE Conference on

作者： Mayara Aragão Pedro González Claudio de Farias Flávia Delicato Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Fluminense Federal University Rio de Janeiro Brazil

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

Federated Learning (FL) has emerged as a promising solution to address challenges in traditional machine learning (ML) regarding data privacy and security. However, training federated models in resource-constrained environments, such as IoT devices, presents challenges due to limited computational resources and complex data. This paper proposes data sampling techniques to optimize federated training in such environments, aiming to reduce training time while maintaining model quality. The study evaluates the impact of data sampling on federated model performance and compares it with traditional approaches. The methodology involves implementing random data selection in client datasets within the context of federated learning and conducting experiments across different configurations to analyze results. The findings provide insights for practical application in real-world scenarios with computational constraints.

关键词： Training Accuracy Federated learning Computational modeling Data models Robustness Proposals Internet of Things Security Standards

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CAWA: Correlation-Based Adaptive Weight Adjustment via Effective Fields for Debiasing

CAWA: Correlation-Based Adaptive Weight Adjustment via Effec...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Kyoungrae Noh San Kim Jaekwang Kim Computer Science and Engineering Sungkyunkwan University Suwon South Korea Convergence Program for Social Innovation Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

In real-world image classification tasks, neural networks often encounter significant challenges due to unexpected or deceptive correlations and biases inherent in the dataset. These biases can emerge from disproportionate data distributions, causing models to generalize poorly to new, unseen data. Such data distribution issues are particularly problematic compared to more balanced datasets because they lead the model to rely on bias attributes rather than intrinsic attributes. Ideally, the model should classify based on intrinsic attributes, but due to the influence of bias attributes, frequent misclassification occurs. Such biases compromise the fairness and accuracy of the model, especially in critical scenarios such as medical diagnosis, autonomous driving, or criminal justice, where misclassification can have significant consequences. To address these challenges, we propose an innovative two-stage approach aimed at mitigating bias more effectively and efficiently.

关键词： Adaptation models Correlation Accuracy Neural networks Training data Data models Medical diagnosis Autonomous vehicles Image classification

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Metacognitive-Based Collaborative programming: A Novel Approach to Enhance Learning Performance in programming Courses 6th

Metacognitive-Based Collaborative Programming: A Novel Appro...

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Proceedings of the 6th International Conference on Innovative Technologies and Learning, ICITL 2023

作者： Li, Wei Tseng, Judy C. R. Cheng, Li-Chen STEM Education Research Center Wenzhou University Zhejiang Province Wenzhou China Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei City Taiwan

ISBN: (纸本)9783031401121

Students’ computational thinking and programming skills may grow due to collaborative programming. But as the researchers have noted, students frequently do not use metacognition to manage their cognitive activities while collaborating, which negatively affects learning. This study created a metacognition-based collaborative programming (MCP) system to improve students’ performance in collaborative programming. A seven-week study examined how the approach affected students’ performance in programming courses. The 88 middle school students were split into two groups: the experimental group received the metacognition-based collaborative programming approach, and the control group received the conventional computer-supported collaborative programming approach. The results indicated that the metacognitive-based collaborative programming approach enhanced students’ academic scores in programming courses and their computational thinking tendencies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Thermal Hall effect in insulating quantum materials

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The Innovation 2022年第5期3卷 3-4页

作者： Shucheng Guo Youming Xu Ran Cheng Jianshi Zhou Xi Chen Department of Electrical and Computer Engineering University of CaliforniaRiversideRiversideCA 92521USA Department of Physics and Astronomy University of CaliforniaRiversideRiversideCA 92521USA Materials Science and Engineering Program Department of Mechanical EngineeringUniversity of Texas at AustinAustinTX 78712USA

The emerging field of quantum materials involves an exciting new class of materials in which charge,spin,orbital,and lattice degrees of freedom are intertwined,exhibiting a plethora of exotic physical *** materials include,but are not limited to,superconductors,topological quantum matter,and systems with frustrated spins,which enable a wide range of potential applications in biomedicine,energy transport and conversion,quantum sensing,and quantum information processing。

关键词： properties. quantum topological

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Forward and Reverse Converters for the Moduli-Set {22q+1, 2q + 2q−1 ± 1}

arXiv

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arXiv 2024年

作者： Jaberipur, Ghassem Nadimi, Bardia Kazemi, R. Lee, Jeong-A The Brain Pool Program for Chosun University Gwangju Korea Republic of The University of South Florida TampaFL United States Computer Engineering The Department of Computer Science and Engineering Shahid Beheshti University Tehran Iran The Department of Computer Engineering Chosun University Korea Republic of

Modulo-(2q + 2q−1 ± 1) adders have recently been implemented using the regular parallel prefix (RPP) architecture, matching the speed of the widely used modulo-(2q ± 1) RPP adders. Consequently, we introduce a new moduli set τ+ = {22q+1, 2q + 2q−1 ± 1}, with over (2q+2)× dynamic range and adder speeds comparable to the conventional τ = {2q, 2q ± 1} set. However, to fully leverage τ+ in residue number system applications, a complete set of circuitries is necessary. This work focuses on the design and implementation of the forward and reverse converters for τ+. These converters consist of four and seven levels of carry-save addition units, culminating in a final modulo-(2q + 2q−1 ± 1) and modulo-(22q+1 + 22q−2 − 1) adder, respectively. Through analytical evaluations and circuit simulations, we demonstrate that the overall performance of a sequence of operations-including residue generation, k additions, and reverse conversion-using τ+ surpasses that of τ when k exceeds a certain practical threshold. Copyright © 2024, The Authors. All rights reserved.

关键词： Adders

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A 3D Dual-band Electrically Small Monopole Antenna for Internet of Sea Applications

A 3D Dual-band Electrically Small Monopole Antenna for Inter...

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2021 IEEE International Symposium on Antennas and Propagation and North American Radio science Meeting, APS/URSI 2021

作者： Liao, Hanguang Bilal, Rana Muhammad Shamim, Atif Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (纸本)9781728146706

Sensing and retrieving data from ocean to land are challenging and expensive tasks, while the Internet of Sea (IoS) concept can help to monitor ocean environment in a low-cost way. In this paper, an Electrically Small (ES) dual-band monopole antenna is proposed for the IoS application. Dual-band operation (GSM-900, LoRa, and BLE bands) and vertical polarization are maintained in a very compact size. The proposed antenna operates as a folded monopole antenna at 900MHz, and as a simple monopole antenna at 2.4 GHz, by combining the top-loaded folded monopole antenna and the 2-layer monopole feeding structure. The proposed antenna has ka of 0.45 at the LB, with a decent polarization purity and radiation efficiency of 54%. The proposed antenna is designed with a low-cost additive manufacturing material system. © 2021 IEEE.

关键词： Monopole antennas

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Human intracortical responses to varying electrical stimulation conditions are separable in low-dimensional subspaces

Human intracortical responses to varying electrical stimulat...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Sun, Samantha Levinson, Lila H. Paschall, Courtnie J. Herron, Jeffrey Weaver, Kurt Hauptman, Jason Ko, Andrew Ojemann, Jeffrey Rao, Rajesh P. N. University of Washington Paul G. Allen School of Computer Science & Engineering Department of Bioengineering SeattleWA98195 United States University of Washington Graduate Program in Neuroscience Paul G. Allen School of Computer Science & Engineering SeattleWA98195 United States University of Washington Paul G. Allen School of Computer Science & Engineering Department of Neurological Surgery SeattleWA98195 United States University of Washington Paul G. Allen School of Computer Science & Engineering Department of Radiology SeattleWA98195 United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Electrical stimulation is a powerful tool for targeted neurorehabilitation, and recent work in adaptive stimulation where stimulation can be adjusted in real-time has shown promise in improving stimulation outcomes and reducing stimulation-induced side effects. Mapping the relationship between electrical stimulation input and neural activity response can help reveal their interactions and can give us tools to iterate and improve on our stimulation protocols. Here, we introduce methods for identifying low-dimensional subspaces of human intracortical responses to electrical stimulation in invasive electroencephalography. In epilepsy patients (n=4) undergoing clinical monitoring, we applied a stimulation protocol of varying amplitude and frequency in 5-second intervals to capture a range of responses to different stimulation conditions. We characterized these responses using time-frequency spectral power, applied baseline subtraction and outlier removal procedures, and performed principal component analysis across frequencies. We identified that intracortical responses to different stimulation conditions can be represented in a 3-dimensional subspace, accounting for more than 95% of the variance. Using support vector machine classification, we demonstrated separability of intracortical responses in different stimulation conditions across subjects, where this separability was contingent on applying baseline subtraction and outlier removal. Our results represent a first step towards building a predictive model of neural response from stimulation input, an important prerequisite for adaptive closed-loop stimulation for targeted neurorehabilitation. © 2022 IEEE.

关键词： Electroencephalography

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Email Spam Detection Using Logistic Regression and Explainable AI

Email Spam Detection Using Logistic Regression and Explainab...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Sumit Kanti Sarker Ratul Bhattacharjee Md Abu Sufian Md Solaiman Ahamed Md Abu Talha Faiza Tasnim K.M. Nazmul Islam Sharia Tasnim Adrita Department of Computer Science & Engineering (CSE) Faculty of Science & Technology (FST) American International University Bangladesh Dhaka Bangladesh Bachelor of Science in Information Technology (BSIT) Cybersecurity Program Concentrations Washington University of Science and Technology (WUST) VA USA Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering Northern University Bangladesh-(NUB) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.

关键词： Logistic regression Explainable AI Filtering Unsolicited e-mail Computational modeling Predictive models Transformers Frequency conversion Feature extraction Tuning

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Overlapped Error Correction Codes in Two-Dimensional Structures

Overlapped Error Correction Codes in Two-Dimensional Structu...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Andrew Fritsch Wagner Savaris Adahil Muniz Giovanna Borba Rodrigo Girardi Jarbas Silveira César Marcon Graduate Program in Computer Science (PPGCC) Pontifical Catholic University of Rio Grande do Sul (PUCRS) Brazil Graduate Program in Teleinformatics Engineering (PPGETI) Federal University of Ceará (UFC) Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

The continuous advancement of communication systems necessitates the development of algorithms capable of identifying and correcting errors that may arise during data transmission and storage. This pursuit of reliability is particularly crucial in critical systems and sectors that are challenging to access, such as space exploration, passenger transportation, and financial services. In this context, the Error Correction Code (ECC) is a fundamental tool for providing a certain degree of reliability to these systems. This research proposes a novel technique to enhance the error correction capacity of ECCs by leveraging region overlapping. Specifically, we propose correcting data areas protected by more than one ECC, which allows for the inference of logical correlations between ECCs, thereby augmenting their error detection and correction capability. Our focus is bidimensional codeword structures, commonly known as 2D-ECCs, which entail a hierarchical arrangement of ECCs. We evaluated the ECC proposal, comparing its error correction and detection capabilities. Through this evaluation, we aim to demonstrate the technique's efficacy in bolstering the reliability and resilience of communication systems, particularly in critical domains where precision and accuracy are paramount.

关键词： Fault tolerance Fault tolerant systems Transportation Error correction codes Error correction Space exploration Proposals Integrated circuit reliability Financial services Resilience

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SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARNING

SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARN...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Liu, Linfeng Juwono, Filbert H. Wong, W.K. Purwanto, Erick Jin, Tingyan Zhen, Yuyang Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing China School of Instrument Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781837242689

In this study, we explore the classification and prediction capabilities of three models-Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)-on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction. © The Institution of engineering & Technology 2024.

关键词： Genetic programming

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