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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Soubih, Abdenour Lahmer, Seyyid Ahmed Abuhamad, Mohammed Abuhmed, Tamer College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Department of Information Engineering University of Padova Padua Italy Department of Computer Science Loyola University Chicago Chicago United States

ISBN: (纸本)9798331507817

Federated Learning (FL) offers a privacy-preserving solution by enabling multiple clients to train a shared model collaboratively without centralizing data. However, the decentralized nature of FL presents challenges, particularly regarding security and performance under adversarial conditions. This paper investigates the effects of poisoning attacks under data heterogeneity. Our experiments evaluate the impact of varying malicious client fractions and poison concentration levels on the accuracy of the model. We explore the effects of poisoning attacks on FedAvg and FedNova models using medical imaging tasks. Our findings reveal that increasing data heterogeneity exacerbates the effects of poisoning, with FedNova demonstrating greater resilience compared to FedAvg. We found that the number of malicious clients plays a more significant role in degrading performance than the ratio of poisoning samples shared by each malicious client, suggesting that even modest levels of poisoning can be tolerated by most algorithms. The study highlights the importance of developing robust defense mechanisms to maintain model performance under adversarial conditions. © 2025 IEEE.

关键词： Federated learning

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Roll for Robot: A Tabletop Role-Playing Game for Designing Socially Assistive Robots for Depression Management 16th

Roll for Robot: A Tabletop Role-Playing Game for Designing ...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Collins, Sawyer Henkel, Kenna Baugus Henkel, Zachary Śabanović, Selma Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States College of Computer Science and Engineering Mississippi State University Mississippi State StarkvilleMS United States

ISBN: (纸本)9789819635214

Imagining and creating a future with a robot tailored to an individual’s needs and wants provides a unique challenge to both designers and perspective users. Using both an existing base zoomorphic robot, TherabotTM in a specially designed tabletop role-playing game, we have explored what it would look like for someone living with depression to go through a "day in the life" with their newly designed robot companion. Inspired by this collaborative co-design technique, eight participants explained their own relationship with depression, identified a character that they connected with, and chose the physical form, sensors, and abilities for a socially assistive robot. Using this character and robot, participants described how they might overcome depression symptoms and identified uses when managing depression symptoms for the robot. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine design

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DeepSD:Sinkhorn Distance Calculated by Different Methods for Few-Shot Learning

DeepSD:Sinkhorn Distance Calculated by Different Methods for...

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Software Engineering and Computer Science (CSECS), International Conference on

作者： Yun Pang Hayati Abd Rahman College of Computing Informatics & Mathematics Universiti Teknologi MARA Malaysia Selangor Malaysia

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Few-Shot Learning (FSL) has emerged as an effective approach for tasks with limited or no labeled samples, showing great potential. However, the challenge remains in efficiently extracting and learning valuable information in a timely manner. In this study, we revisit Few-Shot Learning classification methods from an optimization perspective. We leverage the Sinkhorn Distance (SD) as a measure of similarity between computationally intensive sample features. Moreover, we explore the Sinkhorn Stabilized Distance (SSD) and Unbalanced Sinkhorn Distance (UNSD) to optimize the matching flow between two samples, aiming for the best alignment. To address issues stemming from intra-class variations and the challenge of differentiating foreground and background features, we introduce the Polarized Selective Kernel Attention (PSKA) mechanism. Extensive experiments validate the efficacy of our proposed methods, demonstrating competitive performance across multiple benchmark datasets.

关键词： Computer vision Attention mechanisms Urban planning Transfer learning Feature extraction Transformers Trajectory Few shot learning Kernel Vehicle dynamics

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Balancing Fairness and Performance in Healthcare AI: A Gradient Reconciliation Approach

arXiv

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arXiv 2025年

作者： Wang, Xiaoyang Yang, Christopher C. College of Computing & Informatics Drexel University PhiladelphiaPA19104 United States

The rapid growth of healthcare data and advances in computational power have accelerated the adoption of artificial intelligence (AI) in medicine. However, AI systems deployed without explicit fairness considerations risk exacerbating existing healthcare disparities, potentially leading to inequitable resource allocation and diagnostic disparities across demographic subgroups. To address this challenge, we propose FairGrad, a novel gradient reconciliation framework that automatically balances predictive performance and multi-attribute fairness optimization in healthcare AI models. Our method resolves conflicting optimization objectives by projecting each gradient vector onto the orthogonal plane of the others, thereby regularizing the optimization trajectory to ensure equitable consideration of all objectives. Evaluated on diverse real-world healthcare datasets and predictive tasks—including Substance Use Disorder (SUD) treatment and sepsis mortality—FairGrad achieved statistically significant improvements in multi-attribute fairness metrics (e.g., equalized odds) while maintaining competitive predictive accuracy. These results demonstrate the viability of harmonizing fairness and utility in mission-critical medical AI applications. © 2025, CC BY.

关键词： Occupational diseases

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Identity-Based Signature from Lattices Without Trapdoors 26th

Identity-Based Signature from Lattices Without Trapdoors

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26th International Conference on Information and Communications Security, ICICS 2024

作者： Luo, Pingbin Chen, Xinjian Susilo, Willy Huang, Qiong College of Mathematics and Informatics South China Agricultural University Guangzhou China Guangzhou Key Laboratory of Intelligent Agriculture Guangzhou510642 China School of Computing and Information Technology University of Wollongong Wollongong Australia

ISBN: (纸本)9789819788002

Currently, the majority of lattice-base identity-based signature (LB-IBS) schemes rely on lattice trapdoor technique introduced by Gentry, Peikert, and Vaikuntanathan. However, this approach significantly impacts computational efficiency. In light of this, we present an alternative method for constructing LB-IBS that does not rely on lattice trapdoors and preimage sampling function. We construct an IBS scheme by employing Lyubashevsky’s signature twice, which is essentially a public key delegation from concatenated Schnorr signatures. In order to optimize the signature size, we describe an adaptation of our scheme in module lattices setting and compression technique. Moreover, our new LB-IBS can combine with Dilithium naturally so that it could carry the advantages of the latter such as side-channel attacks protection. We demonstrate that our construction is provably secure against existential forgery on adaptively chosen message and identity attacks in random oracle model. In comparison with the most efficient LB-IBS scheme (to the best of our knowledge), the signature size of our scheme is 52% smaller and runtime of our scheme is up to 32% faster. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quantum cryptography

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IoT-Based Real-Time Water Quality Monitoring and Sensor Calibration for Enhanced Accuracy and Reliability

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 155-170页

作者： Bakar, Azinoor Azida Abu Bakar, Zahari Abu Yusoff, Zakiah Mohd Ibrahim, Mohd Johan Mohamed Mokhtar, Nurkhairany Amyra Zaiton, Siti Nurul’Ain School of Civil Engineering College of Engineering Universiti Teknologi MARA Selangor Malaysia Electrical Engineering Studies College of Engineering Universiti Teknologi MARA Johor Branch Johor Malaysia Civil Engineering Studies College of Engineering Universiti Teknologi MARA Johor Branch Johor Malaysia Mathematical Sciences Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA Johor Branch Johor Malaysia Faculty of Applied Sciences Universiti Teknologi MARA Johor Branch Johor Malaysia

The increasing water pollution levels and the growing demand for clean water necessitate advanced monitoring solutions. This study aims to develop an Internet of Things (IoT)-based real-time water quality monitoring system that addresses the limitations of traditional methods, which are often time-consuming and lack continuous monitoring capabilities. The system integrates pH, temperature, and turbidity sensors with an Arduino microcontroller and NodeMCU for Wi-Fi connectivity, transmitting data to a cloud platform for real-time access and analysis. The methodology involved interfacing these sensors with the microcontroller, displaying LCD data, and providing LED alerts. The findings indicate that the prototype’s readings closely align with laboratory measurements, with minor deviations in pH and temperature values and slightly more significant differences in turbidity readings. This demonstrates the prototype’s reliability and accuracy in real-time environmental monitoring. The conclusion emphasises the system’s potential to enhance water resource management through continuous and accurate tracking, enabling timely interventions. Future recommendations include improving sensor calibration, expanding the range of monitored parameters, and integrating advanced data analytics for predictive capabilities. The system’s real-time data logging capabilities significantly enhance water management by providing timely alerts and enabling proactive responses to water quality issues. The impact of this system is substantial, offering a scalable and cost-effective solution for continuous monitoring, thus contributing to improved water resource management and public health protection. © 2025 by the authors of this article.

关键词： Water well logging

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"Socially Assistive Robot Privacy Model": A Multi-model Approach to Evaluating Socially Assistive Robot Privacy Concerns 16th

"Socially Assistive Robot Privacy Model": A Multi-model Appr...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Collins, Sawyer Stanojević, Čedomir Bennett, Casey Henkel, Zachary Henkel, Kenna Baugus Abbott, Nikki M. Bethel, Cindy L. Śabanović, Selma Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States College of Behavioral Social and Health Sciences Clemson University ClemsonSC United States College of Computing and Digital Media DePaul University ChicagoIL United States Department of Computer Science and Engineering Mississippi State University StarkvilleMS United States

ISBN: (纸本)9789819635214

As socially assistive robots (SARs) enter more diverse care settings, including users’ homes, it is critical to identify the shifting privacy risks and dimensions of privacy this technology and its data collection capabilities may affect. We propose a new model of privacy to address the complex nature of SARs as a multimodal technology within the healthcare space. To construct this new model, we combine a previous three-dimensional model from the healthcare literature and synthesize it with a seven-dimension technology-related model. We then use this new combined model to analyze self-report data from several workshops with prospective users of the dog-like robot Therabot to map out the dimensions of privacy identified as future concerns for clinicians and those living with depression. Finally, we suggest this model can be used in future studies to support the in-depth exploration of privacy implications of SARs within healthcare through discussions about privacy among clinicians, those receiving care, and robot designers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Q-Rung Orthopair Fuzzy Sets-Enhanced FMEA for COVID-19 Risk Assessment

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 81-92页

作者： Abdullah, Lazim Awang, Noor Azzah Qiyas, Muhammad Special Interest Group of Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Mathematics Riphah International University Failsabad Campus Pakistan

Failure Modes and Effects Analysis (FMEA) is a widely used tool for risk analysis, primarily to identify risk factors affecting system quality. Due to the limitations of the traditional FMEA model, several recent models incorporating advanced fuzzy set extensions have been developed to enhance the reliability of risk assessment outcomes. However, most of these models limit expert flexibility in expressing preferences and often overlook the impact of unequal expert weights and the stability of risk ranking results. This study introduces a new FMEA model based on Q-Rung Orthopair Fuzzy Sets (Q-ROFSs), termed Q-ROFSs-FMEA. Q-ROFSs, an extension of intuitionistic fuzzy sets, introduce a new linguistic term. The Q-ROFSs-FMEA model considers the unequal weights of experts, enabling a dynamic representation of expert preferences. These weights and the linguistic evaluation of risk factors are integrated through an aggregation operator, facilitating consensus among experts. The model is applied to a case study on COVID-19 risk factors, revealing that ‘older age’ (risk priority number 0.000012) is the highest risk factor, while ‘gender’ (risk priority number -0.0037) is the lowest. It is found that the ranking of risk factors determined by the Q-ROFSs-FMEA model is obtained as FM1 FM3 FM6 FM4 FM5 FM7 FM8 FM2 . Furthermore, a comparative analysis indicates consistent ranking results across different models, demonstrating the reliability of the proposed model. The case study and comparative analysis validate the effectiveness and applicability of the Q-ROFSsbased risk assessment model. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Failure modes

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Enhancing Question Answering Systems with Semantic Networks and NLP Techniques

Enhancing Question Answering Systems with Semantic Networks ...

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2025 International Conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Selvam, Muthu Amane, Ninad College of Computing and Informatics University of North Carolina at Charlotte CharlotteNC28223 United States Bms Institute of Technology and Management Department of Ai&ml Bangalore India

ISBN: (纸本)9798331538538

In this paper, the following will be discussed and an analysis of how Question Answering (QA) systems has enhanced Information Retrieval using Natural Language Processing (NLP) techniques. However, it has been perceived that models like rule/relation based models, BERT, GPT-3 etc consider only singular context and do not have efficiency in multi-hop reasoning as well as for ambiguous queries. To overcome these difficulties, this study proposes a combined method comprising of an open-world Semantic Networks and Transformer models in NLP. The proposed system improves contextual understanding through structure entity linking and a number of hops by using knowledge graphs. These two models reveal that the proposed model has an accuracy of 0.924 which is much higher than both BERT with 0.823 and GPT-3 with 0.886. Also, the query response time is decreased to 15%, which makes the present system appropriate for use in real time. This is based on validated error analysis we are able to reduce the failure cases resulting from ambiguous queries as well as incorrect entity linking by 60 percent. In addition, an evaluation made by ten people shows better performance rating (4.9/5) in relevance, readability, and logical coherence. The results validate that incorporating transformers with Semantic Networks results in better performance of QA and thus, make it an optimal choice in safety critical fields including healthcare, finance and legal, domains of AI. The future work will include the enhancement of using GNNs and real-time entity disambiguation. © 2025 IEEE.

关键词： Question answering

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