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International Artificial Intelligence and data Processing Symposium (IDAP)

作者： Saied Salem Raza Mukhlis Oguzhan Katar Bilal Ertuğrul Ozal Yildirim Mugahed A. Al-Antari Department of Artificial Intelligence and Data Science College of AI Convergence Sejong University Seoul Korea Department of Software Engineering Faculty of Technology Firat University Elazig Turkey Department of Neurosurgery Faculty of Medicine Firat University Elazığ Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Lumbar Spinal Stenosis (LSS) is a condition where the spinal canal in the lower back becomes narrower, resulting in pressure on the spinal cord and nearby nerves. To predict LSS, neurologists focus on four key regions in MRI axial scans: the Intervertebral Disc (IVD), Thecal Sac (TS), Posterior Element (PE), and the Area between the Anterior and Posterior (AAP) vertebrae. To delineate these regions, we present a novel hybrid ensemble deep learning CAD system for multi-class segmentation, encompassing data collection, preprocessing, automated hyperparameter optimization and selection, segmentation ensemble learning, and both qualitative and quantitative performance evaluation. The core of the proposed pipeline features two ensemble phases: (1) ensemble learning through feature space fusion of three distinct AI models (SwinUNETR, Attention Unet, and UNet++), and (2) ensemble learning via a stacking method of the feature maps outputs from the same AI models used in phase 1. The Mendeley benchmark dataset is employed to train and assess both CAD systems, while a private dataset from Firat University Hospital in Turkey is used for verification and validation. The best achieved results via FFA’s mean dice score is 93.1% and mean IoU is 87.76%. showing the usefulness of using ensemble methods to capture different features from the trained models.

关键词： Deep learning Solid modeling Image segmentation Pathology Hospitals Magnetic resonance imaging Stacking Benchmark testing Ensemble learning Artificial intelligence

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Simulating cyber security management: A gamified approach to executive decision making 22

Simulating cyber security management: A gamified approach to...

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Proceedings of the 37th IEEE/ACM International Conference on Automated software engineering

作者： Adam Tonkin William Kosasih Marthie Grobler Mehwish Nasim Fivecast Australia School of Computer Science The University of Adelaide Australia CSIRO's Data61 Australia Department of Computer Science and Software Engineering The University of Western Australia Australia and College of Science and Engineering Flinders University Australia

ISBN: (纸本)9781450394758

Executive managers are not all equipped with the cyber security expertise necessary to enable them to make business decisions that accurately represent the status and needs of the cyber security side of the business. Unfortunately, the lack of understanding between the business and cyber security domains contribute to structurally endorsed vulnerabilities within a business context, where either the business needs were considered without understanding the impact on cyber security, or alternatively, the cyber security needs were considered without fully understanding the impact this would have on the business strategy and financial stability. To combat this dilemma, a gamified approach to cyber security training for executives is proposed as a solution to not only minimise the realisation of cyber vulnerabilities within a business context, but also to improve business outcomes that are supported by cyber security measures. We developed a serious game software platform, Aurelius, to simulate an executive decision maker’s role in managing the everyday cyber security investment decisions, and linking that to business metrics to incorporate the business and cyber security understanding. Our game includes simulated cyber security attacks that would require the executive decision maker (the player) to respond appropriately. The algorithms underpinning our simulated cyber security game are a product of a complex systems approach, as this most accurately models an executive’s experience. In our design, we set up Aurelius to fulfil eight of the nine criteria specified for a state of the art serious game in the cyber security domain.

关键词： serious game decision making human centric cyber security situational awareness. socio-technical systems simulation

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eSROP Attack: Leveraging Signal Handler to Implement Turing-Complete Attack Under CFI Defense 1

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18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Zhang, Tianning Cai, Miao Zhang, Diming Huang, Hao Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China College of Computer Engineering Jiangsu University of Science and Technology Nanjing China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Signal Return Oriented Programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provided by current operating systems and propose a new variant of SROP attack called enhanced SROP (eSROP) attack. eSROP provides the ability of invoking arbitrary system calls, simulating Turing-complete computation, and even bypassing the fine-grained label-based CFI defense, without modifying the return address and instruction register in the signal frame. Because the signal returns to the interrupted instruction, the shadow stack defense can hardly detect our attack. Signal has strong flexibility which can interrupt the normal control flow. We leverage such flexibility to design a new code reuse attack. To evaluate eSROP, we perform two exploits on two real-world programs, namely Proftpd and Wu-ftpd. In our attacks, adversaries can invoke arbitrary system calls and obtain a root shell. Both attacks succeed within 10 min under strict system defense such as data execution prevention, address space layout randomization, and coarse-grained control flow integrity. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Network security

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Deformation-Aware Segmentation Network Robust to Motion Artifacts for Brain Tissue Segmentation using Disentanglement Learning

arXiv

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arXiv 2024年

作者： Jung, Sunyoung Choi, Yoonseok Al-Masni, Mohammed A. Jung, Minyoung Kim, Dong-Hyun Department of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science College of Software and Convergence Technology Sejong University Seoul Korea Republic of Cognitive Science Research Group Korea Brain Research Institute Daegu Korea Republic of

Motion artifacts caused by prolonged acquisition time are a significant challenge in Magnetic Resonance Imaging (MRI), hindering accurate tissue segmentation. These artifacts appear as blurred images that mimic tissue-like appearances, making segmentation difficult. This study proposes a novel deep learning framework that demonstrates superior performance in both motion correction and robust brain tissue segmentation in the presence of artifacts. The core concept lies in a complementary process: a disentanglement learning network progressively removes artifacts, leading to cleaner images and consequently, more accurate segmentation by a jointly trained motion estimation and segmentation network. This network generates three outputs: a motion-corrected image, a motion deformation map that identifies artifact-affected regions, and a brain tissue segmentation mask. This deformation serves as a guidance mechanism for the disentanglement process, aiding the model in recovering lost information or removing artificial structures introduced by the artifacts. Extensive in-vivo experiments on pediatric motion data demonstrate that our proposed framework outperforms state-of-the-art methods in segmenting motion-corrupted MRI scans. The code is available at https://***/SunYJhxppy/Multi-Net. © 2024, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Enhanced adaptive data rate strategies for energy-efficient Internet of Things communication in LoRaWAN

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International Journal of Communication Systems 2025年第2期38卷

作者： Ali Lodhi, Muhammad Wang, Lei Mahmood, Khalid Farhad, Arshad Chen, Jenhui Kumari, Saru School of Software Technology Dalian University of Technology Dalian China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliu Taiwan Department of Computer Science FAST-National University of Computer and Emerging Sciences Islamabad Pakistan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Mathematics Chaudhary Charan Singh University Meerut India School of Chemical Engineering and Physical Sciences Lovely Professional University Jalandhar India

The long-range wide area network (LoRaWAN) is a standard for the Internet of Things (IoT) because it has low cost, long range, not energy-intensive, and capable of supporting massive end devices (EDs). The adaptive data rate (ADR) adjusts parameters at both EDs and the network server (NS). This includes modifying the transmission spreading factor (SF) and transmit power (TP) to minimize packet errors and optimize transmission performance at the NS. The ADR managed by NS aims to provide reliable and energy-efficient resources (e.g., SF and TP) to EDs by monitoring the packets received from the EDs. However, since the channel condition changes rapidly in LoRaWAN due to mobility, the existing ADR algorithm is unsuitable and results in a significant amount of packet loss and retransmissions causing an increase in energy consumption. In this paper, we enhance the ADR by introducing Kalman filter-based ADR (KF-ADR) and moving median-based ADR (Median-ADR), which estimate the optimal SNR by considering the mobility later used to assign the SF and TP to EDs. The simulation results showed that the proposed techniques outperform the legacy ADRs in terms of convergence period, energy consumption, and packet success ratio. © 2024 John Wiley & Sons Ltd.

关键词： adaptive data rate Internet of Things Kalman filter LoRa LoRaWAN mobility resource allocation

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Enabling Collaborative and Green Generative AI Inference in Edge Networks

Enabling Collaborative and Green Generative AI Inference in ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Meng Tian Zhicheng Liu Chao Qiu Xiaofei Wang Dusit Niyato Victor C. M. Leung College of Intelligence and Computing Tianjin University Tianjin China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm’s ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content.

关键词： Energy consumption Adaptation models Generative AI Computational modeling Image edge detection Green products Collaboration Inference algorithms Energy efficiency Internet of Things

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Diff-SFCT: A Diffusion Model with Spatial-Frequency Cross Transformer for Medical Image Segmentation

Diff-SFCT: A Diffusion Model with Spatial-Frequency Cross Tr...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Yuxuan Ding, Yi Zhu, Guobin Qin, Zhen Pang, Minghui Cao, Mingsheng University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering Chengdu China Yibin Great Technology Co. Ltd. Yibin644000 China Sichuan Provincial People's Hospital Department of Geriatric General Surgery Chengdu610054 China

ISBN: (纸本)9798350337488

Most existing semantic segmentation methods primarily employ supervised learning with discriminative models. Although these methods are straightforward, they overlook the modeling of underlying data distributions. In this paper, we propose a novel medical image segmentation framework called Diff-SFCT based on Diffusion Model. We formulate semantic segmentation as a generative problem for segmentation masks, replacing the conventional pixel-wise discriminative learning with a latent prior learning process to produce more accurate segmentation results. Diff-SFCT employs a backbone network combining Convolutional Neural Network (CNN) and Transformer, and utilizes the local perception of CNN and the global information modeling capability of Transformer. In Diff-SFCT, we design a Semantic Encoder that effectively extracts fine-grained semantic features from real images. Meanwhile, we propose a novel Spatial-Frequency Cross Transformer (SFCT) framework, which can effectively model and interact the global features of the diffuse noise mask and the real semantic features, reducing the domain gap between the two and enhancing the model's representational capacity. Additionally, to preserve spatial and frequency information in the diffusion model, we design a Spatial-Frequency Attention Module (SFAM) as part of the Convolutional Block. This module improves the model's spatial and frequency perception abilities while incurring negligible computational overhead. Experimental results evince that our DiffSFCT substantially outperforms other segmentation methods, exhibiting remarkable performance across various medical image segmentation datasets. © 2023 IEEE.

关键词： Attention mechanism CNN Diffusion Model Medical Image Segmentation Transformer

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Generative Semantic Communication for Joint Image Transmission and Segmentation

arXiv

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arXiv 2024年

作者： Yuan, Weiwen Ren, Jinke Wang, Chongjie Zhang, Ruichen Wei, Jun Kim, Dong In Cui, Shuguang FNii-Shenzhen China SSE China Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

Semantic communication has emerged as a promising technology for enhancing communication efficiency. However, most existing research emphasizes single-task reconstruction, neglecting model adaptability and generalization across multi-task systems. In this paper, we propose a novel generative semantic communication system that supports both image reconstruction and segmentation tasks. Our approach builds upon semantic knowledge bases (KBs) at both the transmitter and receiver, with each semantic KB comprising a source KB and a task KB. The source KB at the transmitter leverages a hierarchical Swin-Transformer, a generative AI scheme, to extract multi-level features from the input image. Concurrently, the counterpart source KB at the receiver utilizes hierarchical residual blocks to generate task-specific knowledge. Furthermore, the task KBs adopt a semantic similarity model to map different task requirements into pre-defined task instructions, thereby facilitating the feature selection of the source KBs. Additionally, we develop a unified residual block-based joint source and channel (JSCC) encoder and two task-specific JSCC decoders to achieve the two image tasks. In particular, a generative diffusion model is adopted to construct the JSCC decoder for the image reconstruction task. Experimental results show that our multi-task generative semantic communication system outperforms previous single-task communication systems in terms of peak signal-to-noise ratio and segmentation accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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The IDC System for Sentiment Classification and Sarcasm Detection in Arabic 6

The IDC System for Sentiment Classification and Sarcasm Dete...

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6th Arabic Natural Language Processing Workshop, WANLP 2021

作者： Israeli, Abraham Nahum, Yotam Fine, Shai Bar, Kfir Data Science Institute Interdisciplinary Center Herzliya Israel Department of Software and Information System Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9781954085091

Sentiment classification and sarcasm detection attract a lot of attention by the NLP research community. However, solving these two problems in Arabic and on the basis of socialnetwork data (i.e., Twitter) is still of lower interest. In this paper we present designated solutions for sentiment classification and sarcasm detection tasks that were introduced as part of a shared task by Abu Farha et al. (2021). We adjust the existing state-of-the-art transformer pretrained models for our needs. In addition, we use a variety of machine-learning techniques such as down-sampling, augmentation, bagging, and usage of meta-features to improve the models performance. We achieve an F1-score of 0.75 over the sentiment classification problem where the F1-score is calculated over the positive and negative classes (the neutral class is not taken into account). We achieve an F1-score of 0.66 over the sarcasm detection problem where the F1-score is calculated over the sarcastic class only. In both cases, the above reported results are evaluated over the ArSarcasm-v2-an extended dataset of the ArSarcasm (Farha and Magdy, 2020) that was introduced as part of the shared task. This reflects an improvement to the state-of-the-art results in both tasks. © WANLP 2021 - 6th Arabic Natural Language Processing Workshop

关键词： Natural language processing systems

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One-to-Multiple Clean-Label Image Camouflage (OmClic) based Backdoor Attack on Deep Learning

arXiv

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arXiv 2023年

作者： Wang, Guohong Ma, Hua Gao, Yansong Abuadbba, Alsharif Zhang, Zhi Kang, Wei Al-Sarawi, Said F. Zhang, Gongxuan Abbott, Derek School of Cyber Science and Engineering Nanjing University of Science and Technology JiangSu Nanjing China School of Electrical and Electronic Engineering The University of Adelaide Australia Department of Computer Science and Software Engineering University of Western Australia Australia Data61 CSIRO Australia

Image camouflage has been utilized to create clean-label poisoned images for implanting backdoor into a DL model. But there exists a crucial limitation that one attack/poisoned image can only fit a single input size of the DL model, which greatly increases its attack budget when attacking multiple commonly adopted input sizes of DL models. This work proposes to constructively craft an attack image through camouflaging but can fit multiple DL models' input sizes simultaneously, namely OmClic. Thus, through OmClic, we are able to always implant a backdoor regardless of which common input size is chosen by the user to train the DL model given the same attack budget (i.e., a fraction of the poisoning rate). With our camouflaging algorithm formulated as a multi-objective optimization, M = 5 input sizes can be concurrently targeted with one attack image, which artifact is retained to be almost visually imperceptible at the same time. Extensive evaluations validate the proposed OmClic can reliably succeed in various settings using diverse types of images. Further experiments on OmClic based backdoor insertion to DL models show that high backdoor performances (i.e., attack success rate and clean data accuracy) are achievable no matter which common input size is randomly chosen by the user to train the model. So that the OmClic based backdoor attack budget is reduced by M× compared to the state-of-the-art camouflage based backdoor attack as a baseline. Significantly, the same set of OmClic based poisonous attack images is transferable to different model architectures for backdoor implant. © 2023, CC BY.

关键词： Deep learning

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