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arXiv 2023年

作者： Melnyk, Pavlo Felsberg, Michael Wadenbäck, Mårten Robinson, Andreas Le, Cuong Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden

In this paper, we utilize hyperspheres and regular n-simplexes and propose an approach to learning deep features equivariant under the transformations of nD reflections and rotations, encompassed by the powerful group of O(n). Namely, we propose O(n)-equivariant neurons with spherical decision surfaces that generalize to any dimension n, which we call Deep Equivariant Hyperspheres. We demonstrate how to combine them in a network that directly operates on the basis of the input points and propose an invariant operator based on the relation between two points and a sphere, which as we show, turns out to be a Gram matrix. Using synthetic and real-world data in nD, we experimentally verify our theoretical contributions and find that our approach is superior to the competing methods for O(n)-equivariant benchmark datasets (classification and regression), demonstrating a favorable speed/performance trade-off. The code is available on GitHub. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

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Incremental Learning-Based YOLOv5 Detector for Efficient labor Protection Products Detection 15th

Incremental Learning-Based YOLOv5 Detector for Efficient Lab...

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15th International Conference on computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Du, Liangao Fang, Yang Deng, Xin Ouyang, Weihua Li, Yan Li, Yahui DEVC Lab Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Electrical and Computer Engineering Inha University Incheon22212 Korea Republic of Editorial Office of Computer Science Chongqing401121 China

ISBN: (纸本)9789819724468

In recent years, the accident rates remain high in the construction industry, and the construction safety is a matter of great concern worldwide. An intelligent and efficient incremental detector that can accurately detect whether the workers properly wear labor protection products, such as Safety helmet, safety belt, reflective clothing, is crucial for construction safety. Currently, the most advanced incremental detectors mostly adopt external fixed region proposal methods based on knowledge distillation, which require a lot of time and cost. Due to the lack of annotations for old categories and region proposal information in single-stage detectors, the detectors usually identify old category targets as background, leading to catastrophic forgetting. To address the above problems, this paper proposes a data label merging algorithm, which uses the detection results of the old model and existing data labels to generate merged labels for training, which compensates for the missing annotations of old category targets in the new dataset and mitigates catastrophic forgetting. At the same time, a recent mean-based exemplar selection algorithm is proposed to select representative samples stored in the old dataset, and this representative sample set will be used for model training to help the model resist forgetting. This method has been implemented in a single-stage detection framework, and its effectiveness has been verified on the LPAD (labor Protection Appliance-Dataset) dataset. Our proposed method improves the average precision score (mAP) of old categories and total categories detections by 6.9% and 6.0% respectively compared with the current state-of-the-art method. The merged labels and representative samples obtained from the proposed method effectively alleviate the forgetting problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Construction industry

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Rapid Autonomy Transfer in Reinforcement Learning with a Single Pre- Trained Critic 36

Rapid Autonomy Transfer in Reinforcement Learning with a Sin...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Karim, Muhammad Faraz Deng, Yunjie Niu, Luyao Ramasubramanian, Bhaskar Alexiou, Michail Sahabandu, Dinuka Poovendran, Radha Mertoguno, Sukarno Georgia Institute of Technology AtlantaGA United States University of Washington Network Security Lab Department of Electrical and Computer Engineering SeattleWA United States Western Washington University Electrical and Computer Engineering BellinghamWA United States Kennesaw State University MariettaGA United States

ISBN: (纸本)9798331527235

Reinforcement learning (RL) is a well-studied framework to solve complex decision-making problems in unknown environments. The actor-critic model in RL facilitates autonomy transfer by allowing agents to iteratively update their policies using ongoing dynamic evaluations of value functions via a critic. In this paper, we examine the impact of using different pretrained critics on the performance of actor-critic algorithms. First, in any single given environment, we show that a pretrained critic can be effective in reducing the duration of an initial training phase, thereby accelerating convergence by a factor of up to 2×. In this setting, we identify the critical range of the number of episodes for which a critic will need to be trained in order for it to be an effective pretrained critic. Second, we show that a critic trained in one environment enables transfer of autonomy by aiding learning of behaviors in a different, yet related environment. We carry out extensive experiments on a bipedal locomotion task in the MuJoCo physics engine to verify our hypotheses. Our results in this paper mark the first step towards demonstrating the role and impact of pretrained critics to achieve rapid autonomy transfer for complex reinforcement learning tasks while minimizing costs associated with retraining in new environments. © 2024 IEEE.

关键词： Reinforcement learning

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Data complexity-based batch sanitization method against poison in distributed learning

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Digital Communications and Networks 2024年第2期10卷 416-428页

作者： Silv Wang Kai Fan Kuan Zhang Hui Li Yintang Yang State Key Laboratory of Integrated Service Networks Xidian UniversityXi'an710126China Department of Electrical and Computer Engineering University of Nebraska-LincolnLincolnNE68588USA Key Lab.of the Minist.of Educ.for Wide Bandgap Semiconductor Materials and Devices Xidian UniversityXi'an710071China

The security of Federated Learning(FL)/Distributed Machine Learning(DML)is gravely threatened by data poisoning attacks,which destroy the usability of the model by contaminating training samples,so such attacks are called causative availability indiscriminate *** the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations,we propose a new supervised batch detection method for poison,which can fleetly sanitize the training dataset before the local model *** design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model,which will be used in an efficient batch hierarchical detection *** model stockpiles knowledge about poison,which can be expanded by retraining to adapt to new *** neither attack-specific nor scenario-specific,our method is applicable to FL/DML or other online or offline scenarios.

关键词： Distributed machine learning security Federated learning Data poisoning attacks Data sanitization Batch detection Data complexity

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Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

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International Journal of Fuzzy Systems 2024年第1期26卷 368-389页

作者： Zavieh, Hadi Javadpour, Amir Ja’fari, Forough Sangaiah, Arun Kumar Slowik, Adam School of Economics and Statistics Guangzhou University Guangzhou510006 China Harbin Institute of Technology Shenzhen China Department of Computer Engineering Sharif University of Technology Tehran Iran International Graduate School of AI National Yunlin University of Science and Technology Douliu Taiwan Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900–347 Portugal Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

With the rapid proliferation of IoT and Cloud networks and the corresponding number of devices, handling incoming requests has become a significant challenge. Task scheduling problems have emerged as a common concern, necessitating the exploration of new methods for request management. This paper proposes a novel approach called the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP). Our method combines the strengths of the Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques to enable the efficient selection of physical and virtual machines while operating in a fuzzy mode. We represent data as triangular fuzzy numbers and employ the alpha-cut method to solve the proposed models. The paper provides a mathematical optimization model for the proposed method and presents a numerical example for illustration. Furthermore, we evaluate the performance of our method in a cloud environment through simulations. The results demonstrate that our approach outperforms existing methods, namely PSO + ACO and FBPSO + FBACO, in terms of key metrics, including energy consumption, execution cost, response time, gain of cost, and makespan. © The Author(s) 2023.

关键词： Markov processes

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Maize EfficientNet Fusion: Advancing Maize Disease Detection with MF-NET 25

Maize EfficientNet Fusion: Advancing Maize Disease Detection...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Khalid, Fatima Hanif, Muhammad Ul Ain, Qurat Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Faculty of Computer Science and Engineering KPK Topi Pakistan Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Aerial Robotics and Vision Lab KPK Topi Pakistan Shifa Tameer-E-Millat University Department of Computing Islamabad Pakistan

ISBN: (纸本)9798350379037

Maize is a vital global crop, essential for food security but highly susceptible to diseases that threaten yield and quality. Traditional methods for detecting these diseases are computationally intensive and rely on high-quality images, limiting their practical use in diverse field conditions. This research addresses these challenges by proposing MF-Net, a novel model that leverages two EfficientNet-b0 architectures with ReLU and Swish activation functions. Our approach involves partially training Model R with frozen early layers using ReLU, and fully training Model S with Swish activation for enhanced optimization and robustness. We employ model concatenation and feature fusion techniques to create a lightweight yet powerful model, complemented by additional layers for improved feature extraction and regularization. Experimental results on the Corn Disease and Severity (CD&S) dataset are compelling: MF-Net achieves a remarkable 95.7% accuracy and 96% precision in disease classification, and 88.3% accuracy and 88.46% precision in severity level detection. These results highlight the model's effectiveness under challenging conditions such as cluttered backgrounds and variable lighting, significantly reducing computation time without compromising accuracy. Hence, MF-Net presents an innovative and efficient solution for maize disease detection and severity assessment, offering significant advancements for agricultural practices and crop management. © 2024 IEEE.

关键词： CD&S Dataset EffiecientNet-b0 Fused Truncated EfficientNet Maize Disease Maize Severity Level

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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

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"We Are Just One Step Away";Embracing’4IR’ With Realistic Transitional Framework Rooted With the Manufacturing Practices of RMG Industries in Bangladesh

"We Are Just One Step Away";Embracing’4IR’ With Realistic ...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Saha, Antara Abeer, Ifti Azad Ahmed, Nova Design Inclusion and Access Lab. Department of Electrical and Computer Engineering North South University Dhaka Division Dhaka Bangladesh

ISBN: (纸本)9798400713958

Ready-made garment (RMG) industries of Bangladesh are dallying to accept the industrial culture of using ‘4IR’ technologies and losing its competitive ground in the market. Consequently, it’s enduring the economic stress of managing RMG-driven financial infrastructure. We conducted a mixed-method study involving 75 participants from 4 different RMG factories. Our findings unveil the ineptitude of the employers in evaluating system’s capacity through operating manners, machines and automations in manufacturing to evolve with ‘4IR’. Whereas, we found equivocal appearance regarding technological exposures covered by numerous insecurities related to transitional decisions and job certainty to embrace ‘4IR’. We highlighted the stimulating factors for commencing ‘4IR’ technology adoption through a flexible transitional framework of digitization. It is to emerge smarter industrial practices ensuring maximum utilisation of their resources and human engagement to enhance the country’s economic landscape that’s also essential for the growth of other RMG-based resource-constrained countries. © 2025 Copyright held by the owner/author(s).

关键词： 4IR Manufacturing Ready-made Garment RMG Technology Adoption Transitional Framework

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Rolling and Day-Ahead Forecasting of Electricity Market Prices: Evaluating the Performance of DL Models with Bayesian Optimization

Rolling and Day-Ahead Forecasting of Electricity Market Pric...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Niafenderi, Sajedeh Darvishi Mandal, Paras The University of Texas at El Paso Department of Electrical and Computer Engineering El PasoTX79968 United States Lab United States

ISBN: (纸本)9798350320169

This paper investigates the application of deep learning (DL) algorithms for short-term electricity market price forecasting which is crucial for cost-effective operation of power systems. We propose four DL models that combine different architectures, including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This further involves a Bayesian Optimization (BO) method for hyperparameter tuning of the two best models to ensure optimal performance, as these models are sensitive to hyperparameters. Moreover, to evaluate the performance of the model, this paper also presents rolling forecasting using the LSTM model. Forecasting capability of the DL models is evaluated by using accuracy measures such as mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE). Further, the DL models are also compared with our previous work. This paper utilizes the hourly data of prices and loads acquired from the PJM electricity market. Our findings indicate that CNN+LSTM and CNN+BiLSTM models outperform the other models for day-ahead forecasting, whereas BiLSTM and CNN+BiLSTM models show better performance for week-ahead price forecasting. © 2023 IEEE.

关键词： Long short-term memory

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SpatialHD: Spatial Transformer Fused with Hyperdimensional Computing for AI Applications 5

SpatialHD: Spatial Transformer Fused with Hyperdimensional C...

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5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Bettayeb, Meriem Hassan, Eman Mohammad, Baker Saleh, Hani Khalifa University of Science and Technology System on Chip Lab Electrical Engineering and Computer Science Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350332674

Brain-inspired computing methods have shown remarkable efficiency and robustness compared to deep neural networks (DNN). In particular, HyperDimensional Computing (HDC) and vision Transformer (ViT) have demonstrated promising achievements in facilitating effective and reliable cognitive learning. This paper proposes SpatialHD, the first framework that combines spatial transformer networks (STN) and HDC. First, SpatialHD exploits the STN, which explicitly allows the spatial manipulation of data within the network. Then, it employs HDC to operate over STN output by mapping feature maps into high-dimensional space, learning abstracted information, and classifying data. In addition, the STN output is resized to generate a smaller input feature map. This further reduces computing complexity and memory storage compared to HDC alone. Finally, to test the model's functionality, we applied spatial HD for image classification, utilizing the MNIST and Fashion-MNIST datasets, using only 25% of the dataset for training. Our results show that SpatialHD improves accuracy by ≈ 8% and enhances efficiency by approximately 2.5x compared to base-HDC. © 2023 IEEE.

关键词： Image classification

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