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International Conference on Trends in Electronics and Informatics (ICEI)

作者： P. Devisri Bhavana Jamalpur Stanly Felix Christian Raj Keerthika Velusamy Ashok Kumar V B. Jegajothi CSE Ramco Institute of Technology Rajapalayam Tamil Nadu Department of CS & AI SR University Warangal Software Systems Coimbatore Institute of Technology peelamedu Civil Aerodrome post Coimbatore 14 Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu SRS Tech Solutions Chennai

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

The swift proliferation of IoT devices in smart home ecosystems has resulted in notable interoperability issues due to the presence of numerous conflicting communication protocols, such as Zigbee, Z-Wave, MQTT, CoAP, Bluetooth, and Wi-Fi. Current solutions frequently depend on static protocol mapping, which proves inadequate for the dynamic nature of smart home environments where network conditions and device needs are in constant flux. In this paper, we introduce the Dynamic IoT Protocol Adaptation Framework (DIP A), which merges Reinforcement Learning (RL) with Multi-Armed Bandit (MAB) optimization to facilitate real-time, adaptive protocol selection. DIPA leverages edge computing for predictive decision-making and incorporates Graph Neural Networks (GNN) to assess protocol compatibility. A middleware-based protocol translation layer guarantees seamless integration across varied IoT systems. We assess the framework using a sandbox populated with actual IoT data to evaluate performance concerning adaptability, latency, energy consumption, and scalability. The findings indicate that DIP A diminishes latency by 35% and enhances energy efficiency by 28% when compared to static adaptation approaches. The proposed framework augments interoperability, optimizes resource management, and fosters flexible communication within smart home environments.

关键词： Protocols Translation Scalability Smart homes Reinforcement learning Graph neural networks Internet of Things Interoperability Optimization Electronics packaging

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DECODING EEG SPEECH PERCEPTION WITH TRANSFORMERS AND VAE-BASED DATA AUGMENTATION

arXiv

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arXiv 2025年

作者： Chen, Terrance Yu-Hao Chen, Yulin Soederhaell, Pontus Agrawal, Sadrishya Shapovalenko, Kateryna Computer Science Department Carnegie Mellon University PittsburghPA15213 United States Information Networking Institute Carnegie Mellon University PittsburghPA15213 United States Computational Finance Carnegie Mellon University PittsburghPA15213 United States Software and Societal Systems Department Carnegie Mellon University PittsburghPA15213 United States Language Technologies Institute Carnegie Mellon University PittsburghPA15213 United States

Decoding speech from non-invasive brain signals, such as electroencephalography (EEG), has the potential to advance brain-computer interfaces (BCIs), with applications in silent communication and assistive technologies for individuals with speech impairments. However, EEG-based speech decoding faces major challenges, such as noisy data, limited datasets, and poor performance on complex tasks like speech perception. This study attempts to address these challenges by employing variational autoencoders (VAEs) for EEG data augmentation to improve data quality and applying a state-of-the-art (SOTA) sequence-to-sequence deep learning architecture, originally successful in electromyography (EMG) tasks, to EEG-based speech decoding. Additionally, we adapt this architecture for word classification tasks. Using the Brennan dataset, which contains EEG recordings of subjects listening to narrated speech, we preprocess the data and evaluate both classification and sequence-to-sequence models for EEG-to-words/sentences tasks. Our experiments show that VAEs have the potential to reconstruct artificial EEG data for augmentation. Meanwhile, our sequence-to-sequence model achieves more promising performance in generating sentences compared to our classification model, though both remain challenging tasks. These findings lay the groundwork for future research on EEG speech perception decoding, with possible extensions to speech production tasks such as silent or imagined *** Codes 68T07, 92C55 Copyright © 2025, The Authors. All rights reserved.

关键词： Brain computer interface

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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MedCoDi-M: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation

arXiv

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arXiv 2025年

作者： Molino, Daniele Feola, Francesco Di Faiella, Eliodoro Fazzini, Deborah Santucci, Domiziana Shen, Linlin Guarrasi, Valerio Soda, Paolo Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Roma Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden Department of Diagnostic Imaging and Stereotactic Radiosurgey Centro Diagnostico Italiano S.p.A. Milano Italy Department of Radiology and Interventional Radiology Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy Research Unit of Radiology and Interventional Radiology Department of Medicine and Surgery Università Campus Bio-Medico di Roma Rome Italy College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Artificial Intelligence is revolutionizing medical practice, enhancing diagnostic accuracy and healthcare delivery. However, its adaptation in medical settings still faces significant challenges, related to data availability and privacy constraints. Synthetic data has emerged as a promising solution to mitigate these issues, addressing data scarcity while preserving privacy. Recently, Latent Diffusion Models have emerged as a powerful tool for generating high-quality synthetic data. Meanwhile, the integration of different modalities has gained interest, emphasizing the need of models capable of handle multimodal medical data. Existing approaches struggle to integrate complementary information and lack the ability to generate modalities simultaneously. To address this challenge, we present MedCoDi-M, a 6.77-billion-parameter model, designed for multimodal medical data generation, that, following Foundation Model paradigm, exploits contrastive learning and large quantity of data to build a shared latent space which capture the relationships between different data modalities. Further, we introduce the Multi-Prompt training technique, which significantly boosts MedCoDi-M’s generation under different settings. We extensively validate MedCoDi-M: first we benchmark it against five competitors on the MIMIC-CXR dataset, a state-of-the-art dataset for Chest X-ray and radiological report generation. Secondly, we perform a Visual Turing Test with expert radiologists to assess the realism and clinical relevance of the generated data, ensuring alignment with real-world scenarios. Finally, we assess the utility of MedCoDi-M in addressing key challenges in the medical field, such as anonymization, data scarcity and imbalance learning. The results are promising, demonstrating the applicability of MedCoDi-M in medical contexts. Project page is at https://***/MedCoDi-M/. © 2025, CC BY-NC-ND.

关键词： Contrastive Learning

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Business Process Remaining Time Prediction Based on Incremental Event Logs

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IEEE Transactions on Services Computing 2025年

作者： Guo, Na Liu, Cong Mo, Qi Cao, Jian Ouyang, Chun Lu, Xixi Zeng, Qingtian Shandong University of Technology School of Electrical and Electronic Engineering Zibo255000 China Nova University of Lisbon NOVA Information Management School Lisbon1070-312 Portugal Shandong University of Technology School of Computer Science and Technology Zibo255000 China Yunnan University School of Software Kunming650091 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200030 China Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Utrecht University Department of Information and Computing Sciences CS Utrecht3584 Netherlands Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China

Predictive Process Monitoring (PPM) aims to predict the future state of running process instances to enable timely interventions to mitigate potential risks. As one of the most fundamental tasks in PPM, process remaining time prediction focuses on preventing timeout occurrences. While various deep learning-based approaches have been developed for this purpose, they often rely on pre-established static prediction models and struggle to maintain accurate predictions when the process undergoes dynamic changes, such as an expanding sales channels. To tackle this challenge, this paper proposes an incremental process remaining time prediction framework by continuously updating the prediction model based on an incremental event log. Specifically, a feature selection strategy is first introduced to extract effective features from event logs. Leveraging effective features can significantly improve the prediction quality by capturing the changes in process information. Then, three incremental log-based updating mechanisms, including period-based updating, quantity-based updating, and concept-drift-based updating, along with a reconstruction strategy, are proposed to dynamically adjust the prediction model in response to business changes. Finally, LSTM, Transformer, and Auto-encoder models are adapted and integrated into the proposed framework. The approach has been implemented and publicly released. Experimental evaluation using nine real-life event logs demonstrate that the proposed framework and its three instantiations (i.e., LSTM-based, Transformer-based, and Auto-encoder-based ones) outperform state-of-the-art techniques in terms of prediction accuracy. © 2008-2012 IEEE.

关键词： incremental event log Predictive process monitoring process mining remaining time prediction

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A Review of Deep-Learning-Based Models for Afaan Oromo Fake News Detection on Social Media Networks 3rd

A Review of Deep-Learning-Based Models for Afaan Oromo Fake ...

引用

3rd International Conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Arega, Kedir Lemma Tune, Kula Kekeba Beyene, Asrat Mulatu Tariku, Wegderes Bune, Nurhussen Menza ICT Infrastructures and Administrator Team Leader Ambo University Ambo Ethiopia Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Department of Information Systems Mizan Tepi University Tepi Ethiopia Project Management System Support Expert Ethio Telecom Addis Ababa Ethiopia

ISBN: (纸本)9783031862953

This study explores modern fake news detection techniques in the Afan Oromo language, aiming to contribute to advancing research in this field. It explores deep learning methods for transformers, including Bidirectional Encoder Representations, Attention, and Generative Adversarial Networks, and their applications in various industries such as machine translation, email spam detection, information extraction, summarization, medical, and question-answering. The paper investigates false news identification, its categories, characteristics, and taxonomy, and compares machine learning and deep learning algorithms for fake news, liars, and corpus datasets. It proposes that future studies use picture data and deeper learning network topologies to decrease erroneous outcomes. The research endeavor aims to illuminate and advance the understanding of the complex difficulties and possibilities that exist in this particular sector, thereby acting as a crucial resource for decision-makers, academics, and professionals, while also encouraging the creation of more effective and trustworthy methodologies that can adequately address and alleviate the dissemination of false information shared on various social media channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China Stevens Institute of Technology Department of Systems and Enterprises United States Hong Kong Metropolitan University School of Science and Technology Hong Kong Hong Kong VinUniversity College of Engineering and Computer Science VinUni-Illinois Smart Health Center Viet Nam King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Biosystems and Machines Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Dhahran Saudi Arabia Uppsala University Department of Game Design Faculty of Arts Sweden Tiangong University School of Software Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer's disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. © 2025 IEEE.

关键词： Generative adversarial networks

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

arXiv

引用

arXiv 2025年

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research United Arab Emirates National Water and Energy Center UAE University Abu Dhabi Al-Ain United Arab Emirates

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model *** Codes 68T01, 68T09, 68M14, 68W10, 68W15 © 2025, CC BY.

关键词： Electronic health record

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CRITICAL BANDWIDTH ALLOCATION FOR THE DELIVERY OF COMPRESSED VIDEO

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computer COMMUNICATIONS 1995年第10期18卷 709-717页

作者： FENG, WC SECHREST, S Software Systems Research Lab Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

The transportation of compressed video data without loss of picture quality requires the network to support large fluctuations in bandwidth requirements. These fluctuations can be smoothed, but straightforward approaches to smoothing can still suffer from excessive buffering requirements, poor buffer utilization and an excessive number of bandwidth changes. This paper introduces critical bandwidth allocation, which reduces the number of bandwidth changes to a very small number, and achieves the maximum effectiveness from client-side buffers. A comparison between critical bandwidth allocation algorithms and other smoothing algorithms is presented, the sensitivity of the algorithm to jitter is examined, and, implications for the design of network services are discussed.

关键词： VIDEO-ON-DEMAND BANDWIDTH ALLOCATION MPEG VIDEO COMPRESSION SMOOTHING

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Feature-Oriented Nonfunctional Requirement Analysis for software Product Line

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Journal of computer science & Technology 2009年第2期24卷 319-338页

作者：彭鑫 Seok-Won Lee 赵文耘 School of Computer Science Fudan University Knowledge-Intensive Software Engineering Research Group Department of Software and Information Systems College of Computing and Informatics University of North Carolina at Charlotte

Domain analysis in software product line （SPL） development provides a basis for core assets design and imple- mentation by a systematic and comprehensive commonality/variability analysis. In feature-oriented SPL methods, products of the domain analysis are domain feature models and corresponding feature decision models to facilitate application-oriented customization. As in requirement analysis for a single system, the domain analysis in the SPL development should con- sider both flmctional and nonfunctional domain requirements. However, the nonfunctional requirements （NFRs） are often neglected in the existing domain analysis methods. In this paper, we propose a context-based method of the NFR analysis for the SPL development. In the method, NFRs are materialized by connecting nonfunctional goals with real-world context, thus NFR elicitation and variability analysis can be performed by context analysis for the whole domain with the assistance of NFR templates and NFR graphs. After the variability analysis, our method integrates both functional and nonfunc- tional perspectives by incorporating the nonfunctional goals and operationalizations into an initial functional feature model. NFR-related constraints are also elicited and integrated. Finally, a decision model with both functional and nonfunctional perspectives is constructed to facilitate application-oriented feature model customization. A computer-aided grading system （CAGS） product line is employed to demonstrate the method throughout the paper.

关键词： software product line nonfunctional requirement domain analysis feature-oriented method variability analysis

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