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International Conference on software engineering (ICSE)

作者： Hao Guan Ying Xiao Jiaying Li Yepang Liu Guangdong Bai The University of Queensland Brisbane Australia Southern University of Science and Technology Shenzhen China UQ-SUSTech Joint Program Microsoft Software Technology Center Asia Beijing China Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems

Due to the great advance in machine learning (ML) techniques, numerous ML models are expanding their application domains in recent years. To adapt for resource-constrained platforms such as mobile and Internet of Things (IoT) devices, pre-trained models are often processed to enhance their efficiency and compactness, using optimization techniques such as pruning and quantization. Similar to the optimization process in other complex systems, e.g., program compilers and databases, optimizations for ML models can contain bugs, leading to severe consequences such as system crashes and financial loss. While bugs in training, compiling and deployment stages have been extensively studied, there is still a lack of systematic understanding and characterization of model optimization bugs (MOBs). In this work, we conduct the first empirical study to identify and characterize MOBs. We collect a comprehensive dataset containing 371 MOBs from TensorFlow and PyTorch, the most extensively used open-source ML frameworks, covering the entire development time span of their optimizers (May 2019 to August 2022). We then investigate the collected bugs from various perspectives, including their symptoms, root causes, life cycles, detection and fixes. Our work unveils the status quo of MOBs in the wild, and reveals their features on which future detection techniques can be based. Our findings also serve as a warning to the developers and the users of ML frameworks, and an appeal to our research community to enact dedicated countermeasures.

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

TechRxiv

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TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

关键词： Reinforcement learning

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Optimized Security Mechanism for publicly Secret Key Sharing over Cloud using Blockchain

Optimized Security Mechanism for publicly Secret Key Sharing...

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2024 International Symposium on Securing Next Generation Systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Byeon, Haewon Quraishi, Aadam Thuniki, Praveen Keshta, Ismail Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Intervention Treatment Institute HoustonTX United States Sr Program Analyst Georgia CummingGA30040 United States Computer Science and Information Systems Department College of Applied Sciences Al Maarefa University Riyadh Saudi Arabia Dr. D. Y. Patii Vidyapeeth Pune Dr. D. Y. Patii School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving it, effectively preventing it from being abused. The crucial sections of the nodes that participated in the critical splicing are made public during the critical recovery stage to prevent them from performing harmful things during the critical recovery stage. Add IDs to the nodes that participated in the crucial splicing during the key distribution stage;a dynamic threshold system is intended to track and update the status of malicious nodes in real time. When the node that possesses the crucial component fails, the owner of the critical component and the main node relocate a key element to the new participating nodes. To safeguard sensitive information. The experimental results show that this system has a key recovery rate of 80% and threshold qualities such as traceability, enforceability, and recoverability. © 2024 Copyright for this paper by its authors.

关键词： Blockchain Encryption Key sharing mechanism Privacy Secret key Security

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AI-Empowered Data Offloading in MEC-Enabled IoV Networks

arXiv

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arXiv 2022年

作者： Fontes, Afonso de Ribeiro, Igor L. Muhammad, Khan Gandomi, Amir H. Gay, Gregory de Albuquerque, Victor Hugo C. Chalmers The University of Gothenburg Gothenburg Sweden University of Fortaleza CE Fortaleza Brazil Department of Software Sejong University Seoul Korea Republic of Faculty of Engineering & Information Technology University of Technology Sydney Australia Graduate Program on Teleinformatics Engineering Federal University of Ceará Fortaleza CE Fortaleza Brazil Graduate Program on Electrical Engineering Federal University of Ceará CE Fortaleza Brazil

Advancements in smart vehicle design have enabled the creation of Internet of Vehicle (IoV) technologies that can utilize the information provided by various sensors and wireless communication to perform complex functionality. Many of these functionalities rely on high computational power and low latency. Mobile Edge Computing (MEC) technologies have been proposed as a way to meet these requirements, as their proximity and decentralization provide unique benefits for networks like real-time communication, higher throughput, and flexibility. Diverse challenges to the process of offloading data in a MEC-enabled IoV network have emerged, such as offloading reliability in highly mobile environments, security for users within the same network, and energy management to keep users from being disincentivized to participate in the network. This article surveys research studies that use AI as part of the data offloading process, categorized based on four main issues: reliability, security, energy management, and service seller profit. Afterward, this article discusses challenges and future perspectives for IoV technologies. © 2022, CC BY-SA.

关键词： Mobile edge computing

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia Computer Science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) Computer-aided diagnosis (CAD)

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Ultrasound-Guided Deep Learning for Regional Nerve Block Anesthesia in Scapular Fracture

Ultrasound-Guided Deep Learning for Regional Nerve Block Ane...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Upasna Joshi Mohan Raparthi Ramswaroop Reddy Pramod Kumar Praveen Thuniki Mohammad Shabaz Delhi Technical Campus Greater Noida India Software Engineer Alphabet Life Science Dallas Texas USA Yellu Independent Research Richmond VA 23233 University of Texas Austin US Ganga Institute of Technology and Management Haryana India Independent Research Sr Program Analyst Georgia Cumming GA USA Model Institute of Engineering and Technology Jammu J&K India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

This study introduces a novel approach to regional nerve block anesthesia in scapular fractures by integrating ultrasound guidance with deep learning techniques. The proposed method leverages advanced imaging modalities and state-of-the-art deep learning models to achieve precise nerve localization amidst complex fracture patterns. Through extensive experimentation and evaluation, we demonstrate the effectiveness of the proposed method in enhancing surgical planning, intraoperative decision-making, and patient safety. This study represents a significant advancement in the field of orthopedic surgery, offering a promising solution for improving outcomes and quality of life for patients with scapular fractures. The method this study suggests for locating nerves in shoulder fractures advances orthopaedic surgery. We can improve, expedite, and streamline procedures with the use of AI and medical imaging. This will improve the quality of life and outcomes for scapular fractures.

关键词： Deep learning Location awareness Ultrasonic imaging Orthopedic surgery Decision making Shoulder Anesthesia

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A Deep Learning Classifier Using Sliding Patches For Detection of Mammographical Findings

A Deep Learning Classifier Using Sliding Patches For Detecti...

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Medical Information Processing and Analysis (SIPAIM), International Symposium on

作者： Diego Mellado Marvin Querales Julio Sotelo Eduardo Godoy Fabian Pardo Scarlett Lever Steren Chabert Rodrigo Salas PhD. Program in Health Sciences and Engineering Universidad de Valparaíso Valparaíso Chile School of Medical Technology Universidad de Valparaíso Valparaíso Chile Departamento de Informática UTFSM Santiago Chile PhD. Program in Applied Software Engineering Universidad de Valparaíso Valparaíso Chile School of Medicine Universidad de Valparaíso Valparaíso Chile School of Biomedical Engineering Universidad de Valparaíso Valparaíso Chile

Mammography is known as one of the best forms to screen possible breast cancer in women, and recently deep learning models have been developed to assist the radiologist in the diagnosis. However, their lack of interpretability has become a significant drawback to their extended use in clinical practice. This paper introduces a novel approach for detecting and localising pathological findings in mammography exams through the use of a EfficientNet-based deep learning model. The model is trained using cropped segments of labelled pathological findings from Vindr Mammography Dataset. Achieving an average F1-score of 72.7 %, and reaching on mass and suspicious calcifications an F1-Score of 79.9 % and 84.5 % respectively. Using this classifier we propose a method to visualise from local information the regions of interest where pathological findings could be present on the complete image. Plus, we describe the limitations regarding area coverage of these patches on the model’s capability of generalization and certainty on its predictions, explaining its functionality.

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FPGA Utilized Implementation of Epileptic Seizure Detection System Based on Wearable Devices using Dynamic Partial Reconfiguration

FPGA Utilized Implementation of Epileptic Seizure Detection ...

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Japan-Egypt International Conference on Electronics, Communications and Computers (JEC-ECC)

作者： Mohamed Fawzy Ahmed Hussien Hassan Mostafa Siemens Digital Industries Software Cairo Egypt Electronics and Electrical Communications Engineering Department Cairo University Giza Egypt Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology October Gardens 6th of October University of Science and technology Giza Egypt

ISBN: (纸本)9781665464642

Unplanned seizures are caused by a disorder in the central nervous system known as epilepsy. Although significant advancements have been made in the realm of non-EEG wearable devices, there is still much room for improvement in the field of EEG-based seizure detection and prediction using ML. The management of epilepsy has a lot of promise to be aided by non-invasive wearable technology. The suggested study intends to design and implement a support vector machine (SVM) classification-based epileptic seizure detection system based on various wearable devices. The proposed technique for detecting seizures accomplishes According to data for seizure detection, our system consistently achieves a sensitivity of 100% and an accuracy of 97%. High level MATLAB model creation is part of the design cycle. Despite the fact that high performance cannot be achieved with just one signal. Although a high performance detection system cannot achieve the requisite sensitivity and accuracy with a single signal, we presented various combining techniques. RTL modelling, design optimization, FPGA implementation, and functional verification are all included in the implementation cycle. The capacity of the FPGA’s partial dynamic reconfiguration is suggested for implementation in order to make better use of the available resources. Comparing the proposed implementation to relevant earlier work, it demonstrated improved utilization.

关键词： Support vector machines Training Performance evaluation Sensitivity Wearable computers Epilepsy Feature extraction

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Developing Eye Tracking Exercise System for Treatment of Lazy Eye

Developing Eye Tracking Exercise System for Treatment of Laz...

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Medical Technologies National Conference (TIPTEKNO)

作者： Kaan Kurtel Melahat Kizil Umuthan Ercan Suzan E. Ozsimsek Hasan Baris Mihci Department of Software Engineering Izmir University of Economics Balcova Izmir Turkey Izmir University of Economics Paramedic Program Vocational School of Health Services Luleå University of Technology MSc. Student in Applied Artificial Intelligence E-commerce Project Manager Macquarie University MSc. Student in Data Science

The aim of this study is to develop a fun and effective computer application game for children with amblyopia and to prevent eye strain in people whose work or lifestyle involves constantly looking at screens. The main motivation is to use children’s enthusiasm for playing video games for the treatment of amblyopia. For this, hardware and software products were brought together, and a software was developed according to software engineering principles. The system was tested with a group of volunteers and necessary alterations were made.

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