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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

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A Zero Trust approach for the cybersecurity of Industrial Control Systems

A Zero Trust approach for the cybersecurity of Industrial Co...

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IEEE international symposium on Network computing and Applications

作者： Claudio Zanasi Federico Magnanini Silvio Russo Michele Colajanni Department of Computer Science and Engineering University of Bologna Italy Department of Engineering "Enzo Ferrari" University of Modena and Reggio Emilia

ISBN: (纸本)9798350397314

Industrial plants are adopting an increasing number of digital interconnected technologies that are enriched by several software applications. The IT/OT convergence offers several benefits in terms of efficiency and flexibility but it opens as many issues in terms of cyber vulnerabilities because industrial plants were not designed to be open to Internet. The frequency of successful cyber attacks shows that typical security solutions are inadequate to the novel complexity of industrial contexts. This novel scenario requires original approaches differing from traditional multi-layer networking solutions that are applicable just to rigid and stable infrastructures. We explore the applicability of Zero Trust architecture (ZTA) principles to the industrial context by designing, implementing and testing an integrated defensive solution. The results obtained through a working prototype show that it is possible to implement a Zero Trust identity-centric approach in an industrial context to increase the security and flexibility of the system while providing complete visibility over the entire network. The proposed approach can be used to strengthen legacy industrial systems that were designed for offline use, and to allow the adoption of innovative technologies that minimize the cyber risk for the overall infrastructure.

关键词： standards organizations Prototypes computer architecture Software Zero Trust Industrial plants Best practices

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Oral Cancer Classification Using Few-Shot Learning with CNN and Siamese Networks

Oral Cancer Classification Using Few-Shot Learning with CNN ...

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IEEE India Conference (INDICON)

作者： Anagha Rajan I R Oviya Department of Sciences Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

This research introduces a comprehensive technique for categorising oral cancer images using deep learning algorithms. The dataset includes a standardized series of grayscale oral cancer images that undergo preprocessing steps, including normalisation, augmentation, and resizing to 299×299 pixels to ensure the model's accuracy and robustness. The pretrained Xception model is primarily based on ImageNet, fine-tuned with the aid of extra dense layers specific to oral cancer classification. Extensive hyperparameter tuning complements the model's performance and achieves high classification accuracy. Additionally, the Siamese network evaluates image pairs and determines their similarity. performance indicators such as precision, recall, and F1 score were used to validate the model. This highlights the robustness of the model. The dual-model architecture provides a collaborative framework that improves classification accuracy and enhances clinical interpretability through similarity analysis. The proposed approach represents a significant advancement in oral cancer detection and lays the foundation for reliable and scalable diagnostic tools in clinical practice.

关键词： Adaptation models Accuracy Transfer learning Robustness Real-time systems Planning Convolutional neural networks Tuning Cancer Residual neural networks

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Overcomplete Deep Low-Rank Subspace Clustering

Overcomplete Deep Low-Rank Subspace Clustering

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international symposium on Distributed computing and Applications to Business, Engineering and Science (DCABES)

作者： Yongmeng Feng Congzhe You School of Computer Engineering Jiangsu University of Technology Changzhou Jiangsu China

ISBN: (纸本)9781665454636

Aiming at the fact that when the input data in the deep subspace clustering networks (DSC) has noise, its robustness is poor, the performance is significantly degraded, and the method has too many learnable parameters, we suggest an overcomplete deep low-rank subspace clustering (ODLRSC). The technique is easy to use, efficient, and has shown to be a great fit for subspace clustering. By inserting a fully connected linear layer and its transposition between the encoder and decoder in our suggested technique, we may automatically put rank restrictions on the learnt representations. Additionally, in order to obtain a more reliable representation of the input data for clustering, the characteristics of the under- and over-complete auto-encoder networks are fused in the encoder. Our technique beats DSC and other clustering algorithms in the field of clustering error, and can sustain high level of quality throughout a broad range of LRRs, according to experimental findings on benchmark datasets.

关键词： Computational modeling Clustering methods Clustering algorithms Benchmark testing Robustness Decoding Distributed computing

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Optimizing Diabetic Foot Ulcer Detection: Leveraging SSD and YOLO architectures

Optimizing Diabetic Foot Ulcer Detection: Leveraging SSD and...

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international symposium on high Capacity Optical Networks and Enabling Technologies, HONET

作者： Muhammad Azeem Muhammad Zaman Adnan Akhunzada Tanzila Kehkashan Imran Ashraf Abdul Rehman Department of Computer Science The University of Lahore Sargodha Pakistan Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Department of Data and Cybersecurity University of Doha for Science and Technology Doha Qatar Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia Faculty of Computer Science The University of Lahore Sargodha Pakistan

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Diabetic foot ulcer (DFU) are a severe complication in diabetic patients, requiring accurate and early diagnosis to prevent further complications such as infection or amputation. To address this, Research employed YOLO and SSD models for automated DFU detection from medical images, comparing their performance based on key evaluation metrics, including accuracy, precision, recall, and F1-score. Results demonstrate that both models perform exceptionally well, with SSD achieving an accuracy of 98.7%, surpassing YOLO's 97.5%. Additionally, SSD exhibited superior precision (99%), recall (98.9%), and F1-score (99.1%), indicating its robustness in detecting ulcerated regions of varying sizes and stages. These results were further contextualized through a comparison with previous studies, where SSD outperformed several CNN-based models, which achieved accuracy ranging from 93.7% to 97.0%. The SSD model's multi-scale object detection capabilities, combined with transfer learning and data augmentation techniques, contributed to its high performance. Overall, this study demonstrates the potential of deploying SSD and YOLO models in clinical settings for early and accurate DFU detection, paving the way for more reliable and timely interventions in diabetic care. The findings underline the significance of advanced object detection methods, offering promising avenues for improving patient outcomes in medical imaging

关键词： YOLO Accuracy Smart cities Computational modeling Transfer learning Robustness Distance measurement Diabetes Medical diagnostic imaging Robots

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Safety and Sustainability Indicators of Variable Speed Limit Control in Intelligent Driver Models

Safety and Sustainability Indicators of Variable Speed Limit...

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international symposium on high Capacity Optical Networks and Enabling Technologies, HONET

作者： Mădălin-Dorin Pop Viorel Cărbune Viorica Sudacevschi Department of Computer and Information Technology Politehnica University of Timişoara Timişoara România Department of Computer Sciences and Systems Engineering Technical University of Moldova Chişinău Republic of Moldova

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

The variable speed limit (VSL) control is responsible for real-time adjustment of speed limits based on traffic conditions in sensor-based environments. Integration of this traffic management strategy with intelligent driver models (IDMs) enables the development of control systems for autonomous vehicles. This article identifies the indicators used for the performance evaluation of VSL strategies implemented in IDM. A classification of these indicators into safety and sustainability categories simplifies the identification of the targeted measures of effectiveness (MOEs) of a proposed VSL strategy, such as safety, mobility, or environmental sustainability. Furthermore, this article reviews existing VSL strategies developed for IDMs and discusses how their assessment was carried out based on identified indicators.

关键词： performance evaluation Smart cities Reviews Robot sensing systems Control systems Real-time systems Safety Sustainable development Autonomous vehicles Vehicles

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Flagger: Cooperative Acceleration for Large-Scale Cross-Silo Federated Learning Aggregation

Flagger: Cooperative Acceleration for Large-Scale Cross-Silo...

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Annual international symposium on computer architecture, ISCA

作者： Xiurui Pan Yuda An Shengwen Liang Bo Mao Mingzhe Zhang Qiao Li Myoungsoo Jung Jie Zhang Computer Hardware and System Evolution Laboratory Peking University Chinese Academy of Sciences Institute of Computing Technology Xiamen University Chinese Academy of Sciences Institute of Information Engineering KAIST and Panmnesia

ISBN: (数字)9798350326581

ISBN: (纸本)9798350326598

Cross-silo federated learning (FL) leverages homomorphic encryption (HE) to obscure the model updates from the clients. However, HE poses the challenges of complex cryptographic computations and inflated ciphertext sizes. As cross-silo FL scales to accommodate larger models and more clients, the overheads of HE can overwhelm a CPU-centric aggregator architecture, including excessive network traffic, enormous data volume, intricate computations, and redundant data movements. Tackling these issues, we propose Flagger, an efficient and high-performance FL aggregator. Flagger meticulously integrates the data processing unit (DPU) with computational storage drives (CSD), employing these two distinct near-data processing (NDP) accelerators as a holistic architecture to collaboratively enhance FL aggregation. With the delicate delegation of complex FL aggregation tasks, we build Flagger-DPU and Flagger-CSD to exploit both in-network and in-storage HE acceleration to streamline FL aggregation. We also implement Flagger-Runtime, a dedicated software layer, to coordinate NDP accelerators and enable direct peer-to-peer data exchanges, markedly reducing data migration burdens. Our evaluation results reveal that Flagger expedites the aggregation in FL training iterations by ${436\%}$ on average, compared with traditional CPU-centric aggregators.

关键词： Training Solid modeling Runtime Federated learning Computational modeling computer architecture Telecommunication traffic

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Kyberator: A high-Efficiency FPGA-Based Multi-Mode CRYstALS-Kyber Accelerator for Quantum-Resistant Security Applications

Kyberator: A High-Efficiency FPGA-Based Multi-Mode CRYSTALS-...

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international SoC Design Conference, ISOCC

作者： Nhat Nguyen Dinh Hoai Luan Pham Vu Trung Duong Le Tuan Hai Vu Van Duy Tran Yasuhiko Nakashima University of Information Technology - VNUHCM Vietnam Nara Institute of Science and Technology Nara Japan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

Hardware development for CRYstALS-Kyber is essential to prevent future quantum computer attacks. However, existing CRYstALS-Kyber hardware often has low performance and lacks the flexibility to support the three operation modes: key generation, encryption, and decryption. To address this issue, we propose the Multi-Mode CRYstALS-Kyber Accelerator, named Kyberator, which supports all three operation modes with high speed and hardware efficiency. Notably, this paper is the first to analyze the real-time performance at an actual system-on-chip (SoC) level on an embedded FPGA platform, demonstrating that the power-delay product of the Kyberator is 4.9 to 10.1 times greater than that of the most powerful current CPUs. Compared with related works on FPGA, the Kyberator is at least 2.1 times better in terms of area-delay product and offers better flexibility.

关键词： Quantum computing Hardware Real-time systems System-on-chip Encryption Security Field programmable gate arrays

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Establishing an Interactive Virtual Library for Medical Manuscript Preservation Using KNN/SVM and an Amazon Elastic MapReduce Model

Establishing an Interactive Virtual Library for Medical Manu...

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ACS/IEEE international Conference on computer Systems and Applications

作者： Hassen Hamdi Rim Zarrouk Ramzi Mahmoudi Narjes Benameur Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL University of Sfax Tunisia Laboratoire Technologie Imagerie Mé dicale LTIM-LR12ES06 FMM FSG University of Gafsa University of Monastir Monastir Tunisie Laboratoire d'Informatique Gaspard-Monge Unité Mixte CNRS-UMLV-ESIEE UMR8049 ESIEE Paris Cité Descartes Université Paris-Est Noisy Le Grand France Laboratoire Technologie Imagerie Médicale - LTIM-LR12ES06 Laboratory of Biophysics and Medical Technologies University of Tunis ElManar Higher Institute of Medical Technologies of Tunis Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

The objective of this study was to assess the viability of establishing an interactive Virtual National Library of Medicine (VNL-M). This research proposes a solution that leverages machine learning algorithms and cloud computing technologies, including advanced techniques such as Hadoop, MapReduce, and Cascading, to virtualize a significant number of medical manuscripts from the National Library of Tunisia. The proposed solution has two phases: •The first presents a hybrid (KNN/SVM) approach for an optical character recognition (OCR) system. Because this technology necessitates high bandwidth and computational capacity, spreading it via distributed architecture or platforms may be a viable alternative for improving performance. •The second entails considering cloud computing as an infrastructure (IaaS) to deploy virtualization techniques for the National Library of Tunisia's “medicine and health” subject area, which belongs to the “natural sciences and mathematics” class in the Dewey Decimal Classification (DDC) system. Furthermore, Cloud storing as a Service (SaaS) is employed for the storing and retrieval of enormous amounts of medical manuscript information. The proposed solution was evaluated by conducting experiments using S3 and Amazon EC2 Elstic Map Reduce with an interesting-scale dataset from a database of manuscript heritage. Lastly, the VNL-M is published as a Web service (VNL-Mweb Service).

关键词： computers Cloud computing Machine learning algorithms Web services Databases Optical character recognition Nearest neighbor methods Libraries Mathematics Virtualization

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Normal and Abnormal Lung Sound Classification for an Embedded System Implementation

Normal and Abnormal Lung Sound Classification for an Embedde...

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international Conference on Electrical Engineering, computing Science and Automatic Control (CCE)

作者： José D. Torres-Rodríguez Blanca Tovar-Corona Álvaro Anzueto-Ríos UPIITA SEPI Instituto Politécnico Nacional Mexico city Mexico UPIITA Instituto Politécnico Nacional Mexico city Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

Breath sounds serve as critical indicators of respiratory health, reflecting air movement, changes in lung tissue, and secretions within the tracheobronchial tree. While auscultation is a common clinical practice for detecting abnormal respiratory sounds, it is inherently subjective and prone to errors. This paper presents a novel methodology for processing respiratory sounds using frequency analysis based on Fourier transform and power spectral density (PSD) with Welch method. It introduces three multilayer perceptron (MLP) architectures designed for the automatic classification of lung sounds into normal and abnormal categories. The proposed approach analyzes 4-second respiratory cycles, employing different window sizes to limit the frequency response output of the PSD method, aiming to reduce the complexity of MLP architectures while maintaining high accuracy. The models were trained and tested on two public datasets, ICBHI-2017 and HF_LUNG_V1, achieving accuracies of 100%, 95%, and 91% for MLP1, MLP2, and MLP3, respectively. These results confirm the effectiveness of this approach in automatically classifying normal and abnormal respiratory sounds using MLP architectures. Furthermore, due to their simplicity, future efforts will focus on deploying these systems on hardware platforms, contributing to the development of portable devices that assist medical staff in identifying abnormal lung sounds.

关键词： performance evaluation Accuracy Embedded systems Lungs computer architecture Multilayer perceptrons Vectors Hardware Wearable devices Diseases

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A Lightweight CNN-Based Pothole Detection Model for Embedded Systems Using Knowledge Distillation 21

A Lightweight CNN-Based Pothole Detection Model for Embedded...

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21st international Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Musa, Aminu Hassan, Mohammed Hamada, Mohamed Kakudi, Habeebah Adamu Amin, Md. Faizul Ibne Watanobe, Yutaka Department of Computer Science Federal University Dutse Nigeria Faculty of Computer Sci. & Info. Technology Bayero University Kano Nigeria Software Engineering Lab University of Aizu Japan Graduate School of Computer Science University of Aizu Japan Division of Information System University of Aizu Japan

ISBN: (纸本)9781643683164

Recent breakthroughs in computer vision have led to the invention of several intelligent systems in different sectors. In transportation, this advancement led to the possibility of proposing autonomous vehicles. This recent technology relies heavily on wireless sensors and Deep learning. For an autonomous vehicle to navigate safely on highways, the vehicle needs equipment to aid with detecting road anomalies such as potholes ahead of time. The massive improvement in computer vision models such as Deep Convolutional Neural networks (DCNN) or vision transformers (ViT) resulted in many success stories and tremendous breakthroughs in object detection tasks;this enabled the use of such models in different application areas. But many of the reported results are theoretical and unrealistic in real-life. Usually, the nature of these models is extensive;they are trained on high-performance computers or cloud computing environments with GPUs, which challenge their usage on edge devices. However, to come up with a light model that can fit into embedded devices, the model size has to be reduced significantly so that the performance will not be affected. Therefore, this paper proposes a lightweight model of pothole detection for an embedded device. The model achieved a state-of-the-art accuracy of 98%, with the number of parameters reduced to more than 70% compared with a deep CNN model;the model can be trained and deployed on embedded devices such as smartphones efficiently. © 2022 The authors and IOS Press. All rights reserved.

关键词： Autonomous vehicles

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