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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

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Deep learning for brain age estimation: A systematic review

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Information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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AdvSwap: Covert Adversarial Perturbation with High Frequency Info-swapping for Autonomous Driving Perception

arXiv

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arXiv 2025年

作者： Huang, Yuanhao Zhang, Qinfan Xing, Jiandong Cheng, Mengyue Yu, Haiyang Ren, Yilong Xiong, Xiao School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beijing100191 China Zhongguancun Laboratory Beijing100094 China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby compromising the AI safety. Some researches focus on creating covert adversarial samples, but existing global noise techniques are detectable and difficult to deceive the human visual system. This paper introduces a novel adversarial attack method, AdvSwap, which creatively utilizes wavelet-based high-frequency information swapping to generate covert adversarial samples and fool the camera. AdvSwap employs invertible neural network for selective high-frequency information swapping, preserving both forward propagation and data integrity. The scheme effectively removes the original label data and incorporates the guidance image data, producing concealed and robust adversarial samples. Experimental evaluations and comparisons on the GTSRB and nuScenes datasets demonstrate that AdvSwap can make concealed attacks on common traffic targets. The generates adversarial samples are also difficult to perceive by humans and algorithms. Meanwhile, the method has strong attacking robustness and attacking transferability. © 2025, CC BY-NC-ND.

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Towards the Influence of Human Observer Eye-Movements on Discriminating Between Perceived Sociability of Different Robots 54

Towards the Influence of Human Observer Eye-Movements on Dis...

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54th International Symposium on Robotics, ISR Europe 2022

作者： Kühnlenz, Kolja Kühnlenz, Barbara Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and Arts CoburgD-96450 Germany Academic Center for Sciences and Humanities Coburg University of Applied Sciences and Arts CoburgD-96450 Germany

ISBN: (纸本)9783800758920

Results from a study on Perceived Sociability of a robot dependent on different gaze behaviors of human observers are presented. A 2x2 between-subjects design is used with independent variables’gaze-behavior’ (static/dynamic) and’robot’ (appearance) of a displayed robot head. Participants are told to fixate a point on a screen, which is either static or moving linearly, while a picture of one of two robot heads is shown in the covert visual attention region of the human observer. The robots differ in the level of human-likeness (mechanistic vs. anthropomorphic). Significant results show, that Perceived Sociability depends on the interaction of gaze-behavior and robot appearance. The findings show, that eye-movements influence covert perception of robot attributes and contribute to a better understanding of gaze dependent perception of robot appearance with potential practical implications of situational perception of anthropomorphic attributes like sociability situationally impacting interaction with the robot. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Eye movements

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Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

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computer Systems science & engineering 2023年第12期47卷 2791-2814页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering the Islamic UniversityNajafIraq

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and *** rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within *** the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information *** paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)***,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant *** by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for ***,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute *** propose a COA-based feature selection approach for medical data classification using machine learning *** approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature *** evaluate the performance of our approach on various medical datasets employing diverse machine learning *** results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification *** study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various *** provide further validation,we conduct detailed experiments on a benchmark

关键词： Association rule mining data classification healthcare data machine learning parameter tuning data mining feature selection MLARMC-HDMS COA stochastic gradient descent Apriori algorithm

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LADRC Based Digital Control DC-DC ASIC Design 3

LADRC Based Digital Control DC-DC ASIC Design

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2022 3rd International Conference on electrical, Electronic Information and Communication engineering, EEICE 2022

作者： Zhang, Wangdong Fan, Bo Lu, Chao Liu, Huimin Tianjin Key Lab of Film Electronics and Communication Devices School of Integrated Circuit Science and Engineering Tianjin University of Technology China Meraki Integrated Corp Tianjin China School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale CarbondaleIL62901 United States

The Internet of Things (IoT) systems require power management modules to have the characteristics of low power consumption, high conversion efficiency, high integration, stable output, and good dynamic response. Due to its ease of tuning and superior control performance, linear active disturbance rejection control (LADRC) is an attractive design option. Yet, to date, due to its high complexity, LADRC has been used in software systems, while little effort has been made in its hardware implementation. In this work, we investigate and explore efficient hardware implementation of the LADRC algorithm. A fully integrated digital LADRC controller for DC-DC Buck converters is synthesized with UMC 55 nm CMOS process. Its input voltage is 5V, stable output voltage is 2V, and rated operating current is 100mA. To our best knowledge, this is the first work to implement LADRC algorithms in hardware. Simulation results show that the LADRC controller is superior to the PID controller in the terms of start-up time, line regulation, load regulation. © Published under licence by IOP Publishing Ltd.

关键词： Controllers

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Investigation of the Temperature Cycles of a 1U CubeSat in Low Earth Orbit 73

Investigation of the Temperature Cycles of a 1U CubeSat in L...

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73rd International Astronautical Congress, IAC 2022

作者： Almazrouei, Aaesha Jarrar, Firas Al-Shaibah, Muneera Vu, Thu Marpu, Prashanth Department of Mechanical Engineering Khalifa University Abu Dhabi127788 United Arab Emirates Yahsat Space Lab Khalifa University Abu Dhabi127788 United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates

This paper is concerned with the thermal housekeeping data generated onboard a 1U CubeSat, MYSTA-1, in low Earth orbit. The actual flight data was collected using 14 temperature sensors and then used to validate a numerical multi-physics model for CubeSats. The model is based on a single isothermal node approach and takes into account the orbital parameters as well as the eclipse and the solar illumination cycles on each surface of the CubeSat. In addition, the model includes the effects of the albedo and Earth Infrared heat fluxes. The presented simulations and results from the validated model were used to draw conclusions on the worst hot and cold case scenarios for MYSAT-1. In addition, a sensitivity analysis was carried out to investigate the assumed specific heat capacity effect on the generated temperature cycles. Copyright © 2022 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Thermoanalysis

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Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning

arXiv

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arXiv 2023年

作者： Guo, Xiaobao Selvaraj, Nithish Muthuchamy Yu, Zitong Kong, Adams Wai-Kin Shen, Bingquan Kot, Alex Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Computer Science and Engineering NTU Singapore School of Electrical & Electronic Engineering NTU Singapore DSO National Laboratories Singapore

Deception detection in conversations is a challenging yet important task, having pivotal applications in many fields such as credibility assessment in business, multimedia anti-frauds, and custom security. Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as the difficulties of learning multimodal features effectively. To address this issue, we introduce DOLOS1, the largest gameshow deception detection dataset with rich deceptive conversations. DOLOS includes 1,675 video clips featuring 213 subjects, and it has been labeled with audio-visual feature annotations. We provide train-test, duration, and gender protocols to investigate the impact of different factors. We benchmark our dataset on previously proposed deception detection approaches. To further improve the performance by fine-tuning fewer parameters, we propose Parameter-Efficient Crossmodal Learning (PECL), where a Uniform Temporal Adapter (UT-Adapter) explores temporal attention in transformer-based architectures, and a crossmodal fusion module, Plug-in Audio-Visual Fusion (PAVF), combines crossmodal information from audio-visual features. Based on the rich fine-grained audio-visual annotations on DOLOS, we also exploit multi-task learning to enhance performance by concurrently predicting deception and audiovisual features. Experimental results demonstrate the desired quality of the DOLOS dataset and the effectiveness of the PECL. The DOLOS dataset and the source codes are available at here. © 2023, CC BY.

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Design and Development of Novel Routing Protocols for Ubiquitous Network

Design and Development of Novel Routing Protocols for Ubiqui...

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International Conference for Convergence of Technology (I2CT)

作者： Chandrashekhar Pomu Chä van Srinivas Talabattula Department of Computer Science and Engineering PES University Bangalore India Applied Photonics Lab Electrical Communication Engineering Indian Institute of Science Bangalore India

In this paper, we designed and developed ECA (Event Condition Action) scheme based novel Improved AODV Position and Speed Aware Routing (ECA-IAODV-PSAR) and Improved AODV Edge Connectivity (ECA-IAODV-EC) routing protocols. In specific, these two routing protocols use the speed and position information of the node received from the Global Positioning System (GPS) to deal with routing control packet overhead during inflated mobility. The primary routing protocol ECA-IAODV-PSAR is an alteration of the PSAR routing protocol. The ECA-IAODV-PSAR protocol set limits flooding of the route query packet to the tiny zone of the ubiquitous network to select the optimal route towards the ultimate destination. The results produced in the simulation demonstrate the feasibility of the scheme proposed.

关键词： Conferences Computational modeling Routing Routing protocols Low latency communication Standards Global Positioning System

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Acute lymphoblastic leukemia Classification Based on Convolutional Neural Network

Acute lymphoblastic leukemia Classification Based on Convolu...

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International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Qile Fan Boan Qu Jingjing Teng Anqi Hui Teoh Teik Toe School of Communication and Information Engineering Nanjing University of Post and Telecommunications Nanjing China School of Electrical and Mechanical Engineering Beijing University of Chemical Technology Beijing China Electrical engineering and the automatization specialty Suzhou University of Science and Technology Suzhou China Computer Science andTechnology Xidian University of Computer Science and Technology Xi’an China NTU Business AI Lab Nanyang Technological University Singapore

Acute lymphoblastic leukemia (ALL) is a type of blood cancer that affects white blood cells, primarily affecting children. Early diagnosis is crucial for successful treatment and recovery. In recent years, convolutional neural networks (CNNs) have been increasingly used in medical image analysis, including the detection and classification of ALL from medical images. In this paper, we propose a model called QCResNet for the classification of ALL from peripheral blood smear images. Our proposed model achieved a high accuracy of 98.9% on a dataset of 15,135 images, outperforming several state-of-the-art methods. Our results demonstrate the potential of QCResNet for accurate and rapid effective diagnosis of acute lymphocytes.

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