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17th Asian Conference on computer Vision, ACCV 2024

作者： Mok, Dahyun Bum, Junghyun Tai, Le Duc Choo, Hyunseung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9789819609000

Diabetic Retinopathy (DR) is a primary cause of blindness, necessitating early detection and diagnosis. This paper focuses on referable DR classification to enhance the applicability of the proposed method in clinical practice. We develop an advanced cross-learning DR classification method leveraging transfer learning and cross-attention mechanisms. The proposed method employs the Swin U-Net architecture to segment lesion maps from DR fundus images. The Swin U-Net segmentation model, enriched with DR lesion insights, is transferred to generate a lesion map. Both the fundus image and its segmented lesion map are used as complementary inputs for the classification model. A cross-attention mechanism is deployed to improve the model’s ability to capture fine-grained details from the input pairs. Our experiments, utilizing two public datasets, FGADR and EyePACS, demonstrate a superior accuracy of 94.6%, surpassing current state-of-the-art methods by 4.4%. To this end, we aim for the proposed method to be seamlessly integrated into clinical workflows, enhancing accuracy and efficiency in identifying referable DR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Friends, Not Foes: Enhancing Attack Surface Metrics of Web Applications with Large Language Models 17

Friends, Not Foes: Enhancing Attack Surface Metrics of Web A...

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17th International Conference on Information Security and Cryptology, ISCTurkiye 2024

作者： Tortumlu, Ahmet Bicakci, Kemal Department of Defence Technologies Istanbul Technical University Istanbul Turkey Informatics Institute and Department of Computer Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9798331533649

The attack surface refers to all potential points of entry in a system that an attacker could exploit. In this work, we present an advanced metric for calculating the attack surface of web applications by leveraging the capabilities of large language models (LLMs) and introducing new features based on recent evaluations of the OWASP Top 10 security risks. Incorporating 28 parameters across 9 main categories and drawing on past studies to address previously identified limitations, this metric provides a comprehensive assessment of the attack surface. The Euclidean norm of the metric allows for quantitative comparisons, enabling precise evaluation and monitoring of security risks. Additionally, we develop a tool to facilitate the calculation of attack surface parameters, with the source code available as open source. The tool features an interface that visualizes the attack surface vector, presenting the metrics of web applications in graphical form. Among its many uses, web security analysts can employ our metric and tool to monitor changes in an application's attack surface across different versions. We also provide recommendations for further improving attack surface metric calculations using LLMs. © 2024 IEEE.

关键词： Open systems

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i-NVMe: Isolated NVMe over TCP for a Containerized Environment 42

i-NVMe: Isolated NVMe over TCP for a Containerized Environme...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Lee, Seongho Yeom, Ikjun Kim, Younghoon College of Computing and Informatics Sungkyunkwan University Department of Computer Science and Engineering Suwon Korea Republic of

ISBN: (纸本)9798350334142

Non-Volatile Memory Express (NVMe) over TCP is an efficient technology for accessing remote Solid State Drives (SSDs);however, it may cause a serious interference issue when used in a containerized environment. In this study, we propose a CPU isolation scheme for NVMe over TCP in such an environment. The proposed scheme measures the CPU usage of the NVMe over TCP worker, charges it to containers in proportion to their NVMe traffic, and schedules containers to ensure isolated sharing of the CPU. However, because the worker runs with a higher priority than normal containers, it may not be possible to achieve CPU isolation with container scheduling alone. To solve this problem, we also control the CPU usage of the worker by throttling NVMe over TCP traffic. The proposed scheme is implemented on a real testbed for evaluation. We perform extensive experiments with various workloads and demonstrate that the scheme can provide CPU isolation even in the presence of excessive NVMe traffic. © 2023 IEEE.

关键词： Containers

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Modeling the Health Status of a Ball Bearing for Predictive Maintenance Purposes 9

Modeling the Health Status of a Ball Bearing for Predictive ...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Karapalidou, Elisavet Efraimidis, Agisilaos Vologiannidis, Stavros Antoniou, Efstathios International Hellenic University Department of Computer Informatics and Telecommunications Engineering Terma Magnisias Serres62124 Greece International Hellenic University Department of Informatics and Electronics Engineering Thessaloniki57400 Greece

ISBN: (纸本)9798350311402

Modern industries are constantly aiming to maximize utilization and performance of their machine equipment and minimize costly and unscheduled downtime. In the recent years, this was made possible with the advancement of technology and the coming of the fourth industrial revolution, also known as Industry 4.0, which introduced the Internet of Things and Artificial Intelligence systems in industrial applications. This allowed the employment of predictive maintenance methods able to assess the health of the equipment and diagnose possible future failures. In this work, an experimental assembly was constructed comprised by an electrical motor, a reduction gear, an axle, a coupler, a bearing and a vibration sensor attached to the latter. Three types of experiments were conducted for the purpose of producing a labeled dataset based on vibration measurements. Specifically, measurements were made during the bearing's working in normal operating state, one with lack and one with excess amount of grease. A preliminary statistical analysis was performed while a Multilayer Perceptron model was employed to automatically predict the status of the bearing. The dataset is freely available at the Zenodo data repository. © 2023 IEEE.

关键词： Ball bearings

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Performance Evaluation of Raspberry Pi Pico for Internet of Things and Its Analysis 4

Performance Evaluation of Raspberry Pi Pico for Internet of ...

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4th International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2024

作者： Adi, Puput Dani Prasetyo Kristiyanto, Arip Sopandi, Ajang Nirmala, Irma Tahir, Muhlis Wijaya, Etistika Yuni Department of Telecommunication Bandung Indonesia Faculty of Computer Sciences Pamulang University Department of Information System Indonesia University of Muhammadiyah Department of Informatics Engineering Tangerang Indonesia Tanjungpura University Department of Computer Systems West Borneo Pontianak Indonesia Trunojoyo University Madura Department of Informatics Education Indonesia

ISBN: (纸本)9798350378368

The massive development of the Internet of Things to date continues to develop a lot of small devices that are integrated such as the Wireless Sensor Network, several experts have conducted research using simulation and practice of WSN, one of the powerful embedded system hardware is the Raspberry Pi Pico, which is a small-sized Raspberry Pi such as Mini Arduino or Micro Arduino. The purpose of developing research in the field of Embedded Systems is to strive in terms of power consumption and dynamism in research, which can be placed on drones and also in various research ideas such as medical for patient comfort. Raspberry Pi RP2040 microcontroller uses an ARM Cortex-M0+ dual-core processor, Raspberry Pi Pico works on a voltage of 1.8 - 5.5 V DC, and can be handled with a 3.7 V DC battery. With its small dimensions, Raspberry Pi pico can be applied to various applicative research. © 2024 IEEE.

关键词： ARM processors

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Toward Utilizing Bidirectional Multi-head Attention Technique for Automatic Correction of Grammatical Errors

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第11期51卷 1886-1897页

作者： Mahmoud, Zeinab Kryvinska, Natalia Abdalsalm, Mohammed Solyman, Aiman Alfatemi, Ali Musyafa, Ahmad School of Computer Science and Technology Wuhan University of Technology China Department of Information Management and Business Systems Comenius University Slovakia Department of Computer Science University of Milan Italy CIS Department Fordham University BronxNY10458 United States Department of Informatics Engineering Pamulang University South Tangerang15310 Indonesia

Automatic Grammar Error Correction (GEC) models identify and correct a wide range of grammatical errors. Various strategies have been proposed for GEC, with the Neural Machine Translation (NMT) approach being the most effective. However, NMT-based GEC models with encoder-decoder layers rely heavily on the highest layer, leading to potential inaccuracies. Additionally, during inference, exposure bias can cause the model to substitute previously targeted words with incorrect alternatives. Another challenge is data scarcity. This paper introduces a GEC model leveraging the seq-to-seq Transformer framework, specifically designed for low-resource languages like Arabic. We propose a method to generate noise in the text to create synthetic parallel data, addressing the data constraints. Inspired by Capsule Networks (CapsNet), we incorporate CapsNet in GEC to dynamically aggregate information from multiple layers. In order to mitigate exposure bias, we incorporated a bidirectional training approach and a regularization term using Kullback-Leibler divergence to align left-to-right and right-to-left models. Experiments on two benchmarks demonstrate that our model outperforms current Arabic GEC models, achieving the highest scores. The code is available on GitHub (https://***/Zainabobied/ArabicGEC). © (2024), (International Association of Engineers). All rights reserved.

关键词： Neural machine translation

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CEGSet: Collection of standardized cause-effect graph specifications 12

CEGSet: Collection of standardized cause-effect graph specif...

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Krupalija, Ehlimana Cogo, Emir Becirovic, Seila Prazina, Irfan Pozderac, Damir Besic, Ingmar University of Sarajevo Faculty of Electrical Engineering Department of Computer Science and Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350322910

Cause-effect graphs are a commonly used black-box testing method, and many different algorithms for converting system requirements to cause-effect graph specifications and deriving test case suites have been proposed. However, in order to test the efficiency of black-box testing algorithms on a variety of cause-effect graphs containing different numbers of nodes, logical relations and dependency constraints, a dataset containing a collection of cause-effect graph specifications created by authors of existing papers is necessary. This paper presents CEGSet, the first collection of existing cause-effect graph specifications. The dataset contains a total of 65 graphs collected from the available relevant literature. The specifications were created by using the ETF-RI-CEG graphical software tool and can be used by future authors of papers focusing on the cause-effect graphing technique. The collected graphs can be re-imported in the tool and used for the desired purposes. The collection also includes the specification of system requirements in the form of natural language from which the cause-effect graphs were derived where possible. This will encourage future work on automatizing the process of converting system requirements to cause-effect graph specifications. © 2023 IEEE.

关键词： Black-box testing

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Reversible data hiding based on pixel segmentation and prediction for improving hiding capacity

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Signal, Image and Video Processing 2024年第6-7期18卷 5043-5059页

作者： Huang, Cheng-Ta Weng, Chi-Yao Zheng, Guo-Ting Weng, Hao-Yu International Bachelor Program in Informatics Yuan Ze University Taoyuan320315 Taiwan Department of Information Management Yuan Ze University Taoyuan320315 Taiwan Department of Computer Science and Information Engineering National Chiayi University Chiayi City600355 Taiwan Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung900391 Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City320317 Taiwan

The idea of the traditional histogram shifting technique is to hide a message within the cover-image pixel distribution. However, the embedding capacity is limited by the peak point occurrences. To solve this problem, many reversible data hiding (RDH) techniques are developed to combine pixel prediction and histogram shifting to increase the embedding payload. In this paper, a RDH scheme based on chessboard prediction and pixel partitioning is proposed for enhancing the hiding payload. Pixel partitioning is used to separate a pixel into two parts: most significant bit and least significant bit. Then, the chessboard predictor is adopted to generate prediction errors and separate the pixels. Thereafter, histogram shifting is applied to embed the secret messages into prediction errors. Due to the high similarity of adjacent pixels in an image, the generated prediction errors are highly close to zero, resulting in a large embedding space. The experimental results indicate that the proposed method has superior performance in embedding capacity with acceptable image visual quality compared to existing schemes since it adopts the adaptive adjustment strategy to achieve this improved balance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Pixels

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Optimization of the test case minimization algorithm based on forward-propagation in cause-effect graphs 22

Optimization of the test case minimization algorithm based o...

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22nd International Symposium INFOTEH-JAHORINA, INFOTEH 2023

作者： Krupalija, Ehlimana Cogo, Emir Bećirović, Šeila Prazina, Irfan Pozderac, Damir Bešić, Ingmar University of Sarajevo Faculty of Electrical Engineering Department of Computer Science and Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9781665475464

Many different methods are used for generating blackbox test case suites. Test case minimization is used for reducing the feasible test case suite size in order to minimize the cost of testing while ensuring maximum fault detection. This paper presents an optimization of the existing test case minimization algorithm based on forward-propagation of the cause-effect graphing method. The algorithm performs test case prioritization based on test case strength, a newly introduced test case selection metric. The optimized version of the minimization algorithm was evaluated by using thirteen different examples from the available literature. In cases where the existing algorithm did not generate the minimum test case subsets, significant improvements of test effect coverage metric values were achieved. Test effect coverage metric values were not improved only in cases where maximum optimization was already achieved by using the existing algorithm. © 2023 IEEE.

关键词： Black-box testing

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A comparative analysis on prototype-based clustering methods 12

A comparative analysis on prototype-based clustering methods

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Rada, Rexhep Bedalli, Erind Shurdhi, Sokol Cico, Betim University of Elbasan 'A. Xhuvani' Department of Informatics Elbasan Albania Barleti University Department of Informatics Tirana Albania Epoka University Computer Engineering Department Tirana Albania

ISBN: (纸本)9798350322910

In the machine learning domain, clustering is a fundamental unsupervised learning operation which aims to partition the instances of a dataset into clusters (i.e, groups, subsets) such that instances within the same cluster are much similar to each other and much different from the other clusters. In the broad spectrum of clustering methods, prototype-based methods characterize each cluster through a prototype (i.e. centroid) and a relocation scheme is employed to iteratively redistribute the instances into the clusters, guided by an objective function. In this paper, several prototype-based methods are brought into focus, including K-means, K-medoids, K-medians, Fuzzy C-means and Kernel K-means. These algorithms are experimentally analyzed on several original benchmark datasets, distorted benchmark datasets and synthetic datasets. The comparative analysis is oriented in two main aspects: accuracy and sensitivity to noise and outliers. © 2023 IEEE.

关键词： Cluster analysis

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