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2024 International Conference on Computer and Applications, ICCA 2024

作者： Kozah, Naney Dornaika, Fadi Charafeddine, Jinan El Jaam, Jihad University of the Basque Country UPV/EHU Department of Computer Science and Artificial Intelligence San Sebastian Spain University of the Basque Country UPV/EHU Ikerbasque Basque Foundation for Science San Sebastian Spain Léonard de Vinci Pôle Universitaire Research Center Paris La Défense92916 France School of Computing and Data Liverpool J. Mores University Liverpool United Kingdom

ISBN: (纸本)9798350367560

Segmentation of medical images is crucial for accu-rate diagnosis, treatment planning and intervention. However, the scarcity of data and the variability of different imaging modalities, including MRI, CT, PET and ultrasound, pose a major challenge for deep learning models. This review discusses traditional and advanced data augmentation techniques used to improve segmentation performance. In addition to geometric transformations such as rotation and scaling, advanced approaches such as Generative Adversarial Networks (GANs) and superpixel-based augmentations are also evaluated. The review also highlights novel methods such as Local-and-Contour Aware Grid Mixing (LCAMix) and Hard and Soft Mixing (HSMix) that preserve anatomical boundaries and effectively augment datasets. The analysis concludes with practical insights for clinical applications and suggests integrating these techniques into data pipelines to increase the generalizability of models. Future research directions focus on DICOM-specific augmentation strategies and optimization of computational requirements to improve clinical applicability. © 2024 IEEE.

关键词： Image segmentation

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Star-Shaped Space of Solutions of the Spherical Negative Perceptron

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Physical Review Letters 2023年第22期131卷 227301-227301页

作者： Brandon Livio Annesi Clarissa Lauditi Carlo Lucibello Enrico M. Malatesta Gabriele Perugini Fabrizio Pittorino Luca Saglietti Department of Computing Sciences Bocconi University 20136 Milano Italy Department of Applied Science and Technology Politecnico di Torino 10129 Torino Italy Bocconi Institute for Data Science and Analytics 20136 Milano Italy Department of Electronics Information and Bioengineering Politecnico di Milano 20125 Milano Italy

Empirical studies on the landscape of neural networks have shown that low-energy configurations are often found in complex connected structures, where zero-energy paths between pairs of distant solutions can be constructed. Here, we consider the spherical negative perceptron, a prototypical nonconvex neural network model framed as a continuous constraint satisfaction problem. We introduce a general analytical method for computing energy barriers in the simplex with vertex configurations sampled from the equilibrium. We find that in the overparametrized regime the solution manifold displays simple connectivity properties. There exists a large geodesically convex component that is attractive for a wide range of optimization dynamics. Inside this region we identify a subset of atypical high-margin solutions that are geodesically connected with most other solutions, giving rise to a star-shaped geometry. We analytically characterize the organization of the connected space of solutions and show numerical evidence of a transition, at larger constraint densities, where the aforementioned simple geodesic connectivity breaks down.

关键词： Optimization problems Artificial neural networks Replica methods

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AI Applications in Medical Reporting and Diagnosis

AI Applications in Medical Reporting and Diagnosis

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Mobile, Intelligent, and Ubiquitous computing Conference (MIUCC), International

作者： Manal Makram Ammar Mohammcd Department of Data Science FGSSR Cairo University Cairo Egypt Dept. of CS FGSSR Cairo University School of Computing and Digital Tech ESLSCA University Cairo Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

The integration of artificial intelligence (AI) in healthcare is revolutionizing diagnosis and patient care by improving clinical documentation and the management of electronic health records that depend on medical image interpretation, increasing accuracy, and reducing time. Egypt ranks first in liver disease and second in liver cancer mortality worldwide in 2020. Large language models, a subset of AI techniques, can assist in disease diagnosis. LLM models with multimodal capabilities can classify and describe patient scan images and extract information from clinical notes. These models can extract vital diagnoses with the support of prompt engineering, as one of these models can answer questions, summarize information, and translate complex medical terminology into plain language, enabling patients to understand their medical reports and diagnoses. There are two primary approaches to achieving this. First, fine-tuning can adapt the model to medical data, which can be resource-intensive. The second approach, pre-trained LLM models can be utilized to leverage pre-trained models to perform the necessary tasks, focusing on effectively using prompts to guide the model for precise and relevant outputs. This study highlights the role of generative AI models by focusing on prompt engineering, and how carefully crafting prompts can enhance the effectiveness of LLM models in medical applications with high accuracy. It demonstrates this through experiments using pre-trained models based on semantic similarity with GPT-4o and BioGPT. Implementing a zero-shot model for liver tumor classification is one of the prompt engineering techniques. The performance metrics achieved were impressive, accuracy, precision, recall, and F1-scores are 88, 81, 88, and 83 percent, respectively.

关键词： Adaptation models Accuracy Biological system modeling Computational modeling Focusing Ubiquitous computing Prompt engineering data mining Medical diagnostic imaging Tumors

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Digital Sovereignty through Africa-Centric Emotional Intelligence: A Proof of Concept for AI-Enhanced Mental Health Support

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Procedia Computer science 2025年 254卷 269-278页

作者： Sontaga G. Forane Absalom E. Ezugwu Kevin Igwe Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom 2520 North-West South Africa Department of Psychology Faculty of Humanities University of Johannesburg Bunting Road Auckland Park Johannesburg 2092 South Africa

Digital sovereignty is vital for enabling communities to control their technological ecosystems, particularly in areas like mental health, where cultural relevance and data autonomy are paramount. This study examines the integration of Africa-centric emotional intelligence principles into AI-driven mental health support systems, while at the same time proposing a culturally adaptive framework with a proof of concept that emphasizes both technological autonomy and cultural authenticity. Grounded in values such as empathy, communal well-being, resilience, and Ubuntu, the conceptualized approach aligns mental health interventions with the lived experience of African communities. The framework incorporates conversational agents and personalized interventions, alongside a prototype system capable of detecting and classifying mental health conditions, such as depression, anxiety, and stress. The culturally aligned design enhances user trust, engagement, and perceived usefulness, By emphasizing local data control and leveraging Indigenous systems, this work promotes digital sovereignty and addresses privacy concerns. Preliminary testing of the prototype demonstrates the feasibility of culturally attuned AI solutions, showing improved user engagement and trust. This paper emphasizes the need for mental health interventions that respect cultural identity while promoting digital autonomy. More so, it advocates for AI systems personalized to regional contexts and calls for mental health solutions that uphold cultural identity while advancing digital self-governance.

关键词： data Sovereignty Digital Sovereignty Mental Health Emotional Intelligence Large Language Models Ubuntu Cognitive Behavioral Therapy Africa-Centric

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Task groupings regularization: data-free meta-learning with heterogeneous pre-trained models 24

Task groupings regularization: data-free meta-learning with ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yongxian Wei Zixuan Hu Li Shen Zhenyi Wang Yu Li Chun Yuan Dacheng Tao Tsinghua Shenzhen International Graduate School Tsinghua University China Tsinghua Shenzhen International Graduate School Tsinghua University China and College of Computing & Data Science Nanyang Technological University Singapore School of Cyber Science and Technology Sun Yatsen University China University of Maryland College Park Department of Computer Science and Engineering The Chinese University of Hong Kong China College of Computing & Data Science Nanyang Technological University Singapore

data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data, enabling the rapid adaptation to new unseen tasks. Current methods often overlook the heterogeneity among pre-trained models, which leads to performance degradation due to task conflicts. In this paper, we empirically and theoretically identify and analyze the model heterogeneity in DFML. We find that model heterogeneity introduces a heterogeneity-homogeneity trade-off, where homogeneous models reduce task conflicts but also increase the overfitting risk. Balancing this trade-off is crucial for learning shared representations across tasks. Based on our findings, we propose Task Groupings Regularization, a novel approach that benefits from model heterogeneity by grouping and aligning conflicting tasks. Specifically, we embed pre-trained models into a task space to compute dissimilarity, and group heterogeneous models together based on this measure. Then, we introduce implicit gradient regularization within each group to mitigate potential conflicts. By encouraging a gradient direction suitable for all tasks, the meta-model captures shared representations that generalize across tasks. Comprehensive experiments showcase the superiority of our approach in multiple benchmarks, effectively tackling the model heterogeneity in challenging multidomain and multi-architecture scenarios.

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Secure Image Steganography Technique based on Hybrid Transforms and Enhanced AES

Secure Image Steganography Technique based on Hybrid Transfo...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Shaik, Syed Basha Devi, D. Sudha Sujithra, M. Sarika, G. Tripathi, Vikas Kapila, Dhiraj K L University Department of Computer Science and Engineering Vijayawada India Coimbatore Institute of Technology Department of Computing-Data Science Coimbatore India Baselios Mathews II College of Engineering Department of Electronics and Communication Engineering Kerala Sasthamkotta India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Lovely Professional University Department of Computer Science and Engineering Phagwara India

ISBN: (纸本)9781665489621

This, work uses a hybrid technique that combines the Modified Advanced Encryption Standard (MAES) and Elliptic Curve Cryptography to encrypt medical images (ECC). This article describes a modified AES for image encryption. For the global cryptosystem, a co-design approach is employed, with MAES processing on the NIOS II processor and hardware-accelerated scalar ECC multiplication. The suggested cryptographic system executes in a significantly shorter amount of time, which is a critical consideration for real implementation. This security assessment was effective, and our trials reveal that our proposed technique simplifies and corrects cryptography's essentials. In actuality, the results of the evaluation reveal that the proposed method is efficient, rapid, and secure. © 2022 IEEE.

关键词： Image enhancement

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Time-Slotted On-Demand Predictive Routing for UAV Networks

Time-Slotted On-Demand Predictive Routing for UAV Networks

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Feng, Houze Wang, Jingjing Chen, Jianrui Hou, Xiangwang Wang, Jiacheng Sun, Geng Niyato, Dusit School of Cyber Science and Technology Beihang University Beijing China Tsinghua University Department of Electronic Engineering Beijing China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9798350368369

Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable link quality and frequently changing network topology pose significant challenges for adopting existing routing protocols in mobile ad hoc networks (MANETs). In this paper, we propose a time-slotted on-demand predictive (TSDP) routing protocol designed specifically for UAV networks. The TSDP protocol introduces a novel approach to route selection by incorporating multiple criteria, including delivery ratio, adjacent degree, and mobility prediction factor, to ensure reliable and efficient data transmission. By addressing high latency in route discovery and excessive broadcast overhead, TSDP employs a time-slotted communication mechanism that reduces packet drop rates and enhances route stability. Simulation results demonstrate that TSDP consistently outperforms ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) protocols in terms of throughput, packet delivery ratio, end-to-end delay, and overhead, particularly in highly dynamic network environments. © 2025 IEEE.

关键词： Drones

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Convergence Analysis of Hierarchical Split Federated Learning

Convergence Analysis of Hierarchical Split Federated Learnin...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Hualei Zhang Jun Du Xiangwang Hou Chunxiao Jiang Jintao Wang Dusit Niyato Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Federated Learning (FL) enables distributed intelligence in Internet of Things (IoT) networks, facilitating decentralized machine learning without the need for exchanging raw data. However, the growing complexity of training models significantly hinders their deployment on resource-constrained IoT devices. To address this challenge, Split Federated Learning (SFL) has emerged as a promising solution by partitioning the entire model into client-side and server-side sub-models to alleviate the computational burden on IoT devices. Considering that the client-edge-cloud architecture can enhance data privacy, support connections to a wider range of devices, and reduce communication costs, we explore a hierarchical SFL (HierSFL) system. This system is supported by a HierSFL algorithm that allows for different aggregation frequencies between the client-side and server-side sub-models. Then, we present a convergence analysis of HierSFL that quantifies the effects of client-side and server-side model aggregation on learning performance, providing a theoretical foundation. Empirical experiments verify the theoretical analysis and demonstrate the superiority of the hierarchical architecture within a wireless IoT network. In particular, it is validated that adopting different aggregation frequencies can enhance the training performance. Moreover, the HierSFL algorithm outperforms traditional hierarchical FL algorithm, achieving superior test accuracy in a shorter time.

关键词： Training Wireless communication Analytical models Accuracy Federated learning Computational modeling Computer architecture Partitioning algorithms Internet of Things Convergence

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Federated Model Heterogeneous Matryoshka Representation Learning

arXiv

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arXiv 2024年

作者： Yi, Liping Yu, Han Ren, Chao Wang, Gang Liu, Xiaoguang Li, Xiaoxiao College of Computer Science TMCC SysNet DISSec GTIISC Nankai University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada

Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively train models with heterogeneous structures in a distributed fashion. However, existing MHeteroFL methods rely on training loss to transfer knowledge between the client model and the server model, resulting in limited knowledge exchange. To address this limitation, we propose the Federated model heterogeneous Matryoshka Representation Learning (FedMRL) approach for supervised learning tasks. It adds an auxiliary small homogeneous model shared by clients with heterogeneous local models. (1) The generalized and personalized representations extracted by the two models’ feature extractors are fused by a personalized lightweight representation projector. This step enables representation fusion to adapt to local data distribution. (2) The fused representation is then used to construct Matryoshka representations with multi-dimensional and multi-granular embedded representations learned by the global homogeneous model header and the local heterogeneous model header. This step facilitates multi-perspective representation learning and improves model learning capability. Theoretical analysis shows that FedMRL achieves a O(1/T) non-convex convergence rate. Extensive experiments on benchmark datasets demonstrate its superior model accuracy with low communication and computational costs compared to seven state-of-the-art baselines. It achieves up to 8.48% and 24.94% accuracy improvement compared with the state-of-the-art and the best same-category baseline, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge management

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Bounding the number of reticulation events for displaying multiple trees in a phylogenetic network

arXiv

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arXiv 2024年

作者： Wu, Yufeng Zhang, Louxin School of Computing University of Connecticut StorrsCT06269 United States Department of Mathematics Center for Data Science and Machine Learning National University of Singapore Singapore119076 Singapore

Reconstructing a parsimonious phylogenetic network that displays multiple phylogenetic trees is an important problem in phylogenetics, where the complexity of the inferred networks is measured by reticulation numbers. The reticulation number for a set of trees is defined as the minimum number of reticulations in a phylogenetic network that displays those trees. A mathematical problem is bounding the reticulation number for multiple trees over a fixed number of taxa. While this problem has been extensively studied for two trees, much less is known about the upper bounds on the reticulation numbers for three or more arbitrary trees. In this paper, we present a few non-trivial upper bounds on reticulation numbers for three or more *** Codes 5C30 © 2024, CC BY.

关键词： Abiotic

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