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20th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Butt, Imtiaz Ahmed Bo, Li Butt, Muhammad Hassaan Farooq Tassew, Megabiaw Tewodros Bin Akhtar Khan, Muhammad Usama School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi'an710072 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Software Northwestern Polytechnical University Shaanxi Xi'an710072 China Comsats University Islamabad Department of Computing Wah Campus Wah47040 Pakistan

ISBN: (纸本)9798350318982

In the domain of hyperspectral imaging classification (HSIC) for remote sensing, recent advancements in deep learning have proven transformative. Convolutional neural networks have demonstrated immense potential in resolving these issues of HSIC, including insufficient labeled data, redundant spatial and spectral features, and overfitting. Traditional convolutional neural networks (CNNs) have effectively extracted spectral and spatial features, but 2D CNNs are limited in spatial modeling, while 3D CNNs alone struggle to distinguish spectral and spatial characteristics. Given that the accuracy of HSIC hinges on both spatial and spectral information, we proposed a hybrid-CNN model designed to mitigate the constraints of 2D and 3D CNNs. Our approach involves leveraging hybrid CNNs with spatial and channel attention (CA) mechanisms to address challenges like overfitting and model complexity. The proposed framework also improves generalization performance compared to 2D or 3D CNNs alone. Experiments were conducted on open datasets from Pavia University, Indian Pines, and Salinas to validate the proposed approach. The results demonstrate the efficacy of the hybrid CNN model with spatial and CA in consistently producing excellent classification results through thorough analyses with different deep-learning models. © 2023 IEEE.

关键词： Remote sensing

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Correction: Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions

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Discover Sustainability 2025年第1期6卷 1-2页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Khan, Muhammad Amir Rehman, Ateeq Ur Saeed, Mamoon M. Hamam, Habib Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Pakistan School of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Computer Science School Education Department Government of Punjab Layyah Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia School of Computing Gachon University Seongnam Republic of Korea Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Sana’a Yemen Faculty of Engineering Uni de Moncton Moncton Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia

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Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

arXiv

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arXiv 2025年

作者： Zhang, Xiyue Xue, Xiaoyong Du, Xiaoning Xie, Xiaofei Liu, Yang Sun, Meng School of Computer Science University of Bristol United Kingdom Department of Information and Computing Science School of Mathematical Science Peking University China Department of Software Systems and Cybersecurity Monash University Australia School of Computing and Information Systems Singapore Management University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads. © 2025, CC BY.

关键词： Federated learning

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Gene selection based on adaptive neighborhood-preserving multi-objective particle swarm optimization

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PeerJ Computer Science 2025年 11卷 1-35页

作者： Mehta, Sumet Sohail, Muhammad Twala, Bhekisipho Ullah, Asad Ullah, Fasee Khan, Arfat Ahmad Ling, Qinghua School of Computer Science & Communication Engineering Jiangsu University Jiangsu Zhenjiang China Department of Computer Software Engineering Military College of Signals NUST Islamabad Islamabad Pakistan Faculty of Information and Communication Technology Tshwane University of Technology Pretoria West Pretoria South Africa The Department of Computing Universiti Teknologi PETRONAS Perak Darul Ridzuan Seri Iskandar Malaysia Department of Computer Science College of Computing Khon Kaen University Khon Kaen Khon Kaen Thailand School of Computer Science and Engineering Jiangsu University of Science & Technology Jiangsu Zhenjiang China

The analysis of high-dimensional microarray gene expression data presents critical challenges, including excessive dimensionality, increased computational burden, and sensitivity to random initialization. Traditional optimization algorithms often produce inconsistent and suboptimal results, while failing to preserve local data structures limiting both predictive accuracy and biological interpretability. To address these limitations, this study proposes an adaptive neighborhood-preserving multi-objective particle swarm optimization (ANPMOPSO) framework for gene selection. ANPMOPSO introduces four key innovations: (1) a weighted neighborhood-preserving ensemble embedding (WNPEE) technique for dimensionality reduction that retains local structure;(2) Sobol sequence (SS) initialization to enhance population diversity and convergence stability;(3) a differential evolution (DE)-based adaptive velocity update to dynamically balance exploration and exploitation;and (4) a novel ranking strategy that combines Pareto dominance with neighborhood preservation quality to prioritize biologically meaningful gene subsets. Experimental evaluations on six benchmark microarray datasets and eleven multi-modal test functions (MMFs) demonstrate that ANPMOPSO consistently outperforms state-of-the-art methods. For example, it achieves 100% classification accuracy on Leukemia and Small-Round-Blue-Cell Tumor (SRBCT) using only 3–5 genes, improving accuracy by 5–15% over competitors while reducing gene subsets by 40–60%. Additionally, on MMFs, ANPMOPSO attains superior hypervolume values (e.g., 1.0617 ± 0.2225 on MMF1, approximately 10–20% higher than competitors), confirming its robustness in balancing convergence and diversity. Although the method incurs higher training time due to its structural and adaptive components, it achieves a strong trade-off between computational cost and biological relevance, making it a promising tool for high-dimensional gene selection in bioinformatics. © 2025

关键词： Particle swarm optimization (PSO)

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A First Look at Information Highlighting in Stack Overflow Answers

SSRN

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SSRN 2023年

作者： Ahmed, Shahla Shaan Wang, Shaowei Tian, Yuan Chen, Tse-Hsun Zhang, Haoxiang Department of Computer Science University of Manitoba Canada School of Computing Queen’s University Canada Department of Computer Science and Software Engineering Concordia University Canada Huawei Canada Canada

Context: Navigating the knowledge of Stack Overflow (SO) remains challenging. To make the posts vivid to users, SO allows users to write and edit posts with Markdown or HTML so that users can leverage various formatting styles (e.g., bold, italic, and code) to highlight the important information. Nonetheless, there have been limited studies on the highlighted ***: We carried out the first large-scale exploratory study on the information highlighted in SO answers in our recent study. To extend our previous study, we develop approaches to automatically recommend highlighted content with formatting styles using neural network architectures initially designed for the Named Entity Recognition ***: In this paper, we studied 31,169,429 answers of Stack Overflow. For training recommendation models, we choose CNN and BERT models for each type of formatting (i.e., Bold, Italic, Code, and Heading) using the information highlighting dataset we collected from SO answers. Results: Our models based on CNN architecture achieve precision ranging from 0.71 to 0.82. The trained model for automatic code content highlighting achieves a recall of 0.73 and an F1 score of 0.71, outperforming the trained models for other formatting styles. The BERT models have even lower recalls and F1 scores than the CNN models. Our analysis of failure cases indicates that the majority of the failure cases are missing identification (i.e., the model misses the content that is supposed to be highlighted) due to the models tend to learn the frequently highlighted words while struggling to learn less frequent ***: Our findings suggest that it is possible to develop recommendation models for highlighting information for answers with different formatting styles on Stack Overflow. © 2023, The Authors. All rights reserved.

关键词： Network architecture

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Energy-Efficient Dynamic Asynchronous Federated Learning in Mobile Edge computing Networks

Energy-Efficient Dynamic Asynchronous Federated Learning in ...

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IEEE International Conference on Communications (ICC)

作者： Guozeng Xu Xiuhua Li Hui Li Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

To break data silos and address the challenge of green communication, federated learning (FL) is widely used at network edges to train deep learning models in mobile edge computing (MEC) networks. However, many existing FL algorithms do not fully consider the dynamic environment, resulting in slower convergence of the model and larger training energy consumption. In this paper, we design a dynamic asynchronous federated learning (DAFL) model to improve the efficiency of FL in MEC networks. Specifically, we dynamically choose a certain number of mobile devices (MDs) by their arrival order to participate in the global aggregation at each epoch. Meanwhile, we analyze the energy consumption model of local update and upload update, and formulate the problem as a dynamic sequential decision problem to minimize the energy consumption, which is NP-hard. To address it, we propose an energy-efficient algorithm based on deep reinforcement learning named DDAFL, to intelligently determine the number of MDs participating in global aggregation according to the state of MEC networks at each epoch. Compared with baseline schemes, the proposed algorithm can significantly reduce energy consumption and accelerate model convergence.

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Unsupervised clustering applied to the optimization of a Case-based Reasoning system for the selection of optimal image explanation methods 30

Unsupervised clustering applied to the optimization of a Cas...

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30th International Conference on Case-Based Reasoning Workshop, ICCBR-WS 2022

作者： Brito-Borges, Esteban E. Orozco-Del-Castillo, Mauricio G. Recio-García, Juan A. Tecnológico Nacional de México IT de Mérida Department of Systems and Computing Merida Mexico Association for the Advancement of Artificial Intelligence Mexico Department of Software Engineering and Artificial Intelligence Instituto de Tecnologías del Conocimiento Universidad Complutense de Madrid Spain

The goal of this paper is to develop a cluster-based retrieval process to select the optimal explanation method for a given image and its corresponding classification by a neural network model. We propose the use of a density clustering method to organize a case base consisting of images labeled according to their optimal explanation method. This approach presents a prediction accuracy similar to a standard nearest-neighbor method, but significantly reducing the required retrieval time. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Neural network models

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Applying explanation methods for the iterative refinement of an ANN-based depression screening tool 30

Applying explanation methods for the iterative refinement of...

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30th International Conference on Case-Based Reasoning Workshop, ICCBR-WS 2022

作者： Sosa-Espadas, Cristian E. Cetina-Aguilar, Manuel Soladrero, Jose A. Darias, Jesus M. Brito-Borges, Esteban E. Cuevas-Cuevas, Nora L. Orozco-Del-Castillo, Mauricio G. Tecnológico Nacional de México IT de Mérida Department of Systems and Computing Merida Mexico Association for the Advancement of Artificial Intelligence Mexico Department of Software Engineering and Artificial Intelligence Instituto de Tecnologías del Conocimiento Universidad Complutense de Madrid Spain

Depression, as one of many mental health disorders, is a serious health and economy problem, affecting over 300 million people of all ages. The diagnosis of depression is a very complex and time-consuming task for mental health professionals, who usually rely on self-report questionnaires as a screening process. However, the items used in these questionnaires are sometimes subjective, particularly to certain demographics, and could require much time and effort from the patient. In recent years, artificial intelligence techniques, such as artificial neural networks, have been commonly used to screen for depression, however, they operate as black-box models, i.e., they lack explainability and interpretability which are mandatory in health-related fields. In this work we propose not only an artificial neural network, but also a set of explainable artificial intelligence techniques to refine a large set of items from a psychological questionnaire into a more concise, explainable one. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Neural networks

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IREX: A reusable process for the iterative refinement and explanation of classification models 30

IREX: A reusable process for the iterative refinement and ex...

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30th International Conference on Case-Based Reasoning Workshop, ICCBR-WS 2022

作者： Sosa-Espadas, Cristian E. Cetina-Aguilar, Manuel Soladrero, Jose A. Darias, Jesus M. Brito-Borges, Esteban E. Cuevas-Cuevas, Nora L. Orozco-Del-Castillo, Mauricio G. Tecnológico Nacional de México IT de Mérida Department of Systems and Computing Merida Mexico AAAIMX Student Chapter at Yucatan Mexico Association for the Advancement of Artificial Intelligence Mexico Department of Software Engineering and Artificial Intelligence Universidad Complutense de Madrid Spain

This paper presents IREX: a reusable method for the Iterative Refinement and EXplanation of classification models. It has been designed for domain-expert users -without machine learning skills- that need to understand and improve classification models. This way, it only requires the expected classification outcomes given by a domain expert. IREX proposes a smart combination of XAI methods that identifies potential inconsistencies in the model, explaining the causes to the user. Following a cycle analogous to CBR, a set of candidate anomalous variables are identified (retrieved) and proposed for its revision by an expert user. Once revision is confirmed, the model is purged and retrained for further optimization. This is a novel process where explanation methods are not only applied to explore a black-box model, but also to detect which input variables led to misclassifications, proposing and explaining their negative impact to the domain-expert user. We propose an automatic evaluation approach based on computing the number of anomalous input variables that the expert was able to identify and its comparison to the evolution of the classification model's performance. Then we apply this evaluation method to demonstrate the performance of the proposal on the given dataset. Finally, we provide a reusable implementation that can be directly applied to other classification models and domains. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Iterative methods

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PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud computing

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Procedia Computer Science 2024年 238卷 385-395页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios.

关键词： cloud computing edge computing dataset generation energy-efficiency green computing machine learning internet of things power consumption modeling resource management scheduling

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