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8th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2020

作者： Baghini, Mohammad Hossein Shojaei Janfada, Behrooz Bidgoli, Behrooz Minaei University of Science and Technology Data Mining Lab Faculty of Computer Engineering Tehran Iran

ISBN: (纸本)9781728173016

FrameNet is a lexical research project that produces a glossary containing very detailed information about syntax (semantic relationships of specific English words such as verbs, nouns, and adjectives). Both human and computer users can use this glossary, so it has become an essential lexical knowledge base for use in fields such as natural language processing and vocabulary semantics. Due to the importance of FrameNet in natural language processing, there is also a need to create FrameNet in languages other than English. There have been few attempts to produce a FrameNet in Persian so far. On the other hand, the existence of an efficient Persian FrameNet can help with the processing of natural language in Islamic texts. In this paper, we present a novel approach to building our first version of the Persian FrameNet. We will then show that although this version is in its infancy, it can be comparable to the FrameNet of some other important languages in terms of frame coverage. © 2020 IEEE.

关键词： Semantics

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Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach

arXiv

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arXiv 2023年

作者： Karimijafarbigloo, Sanaz Azad, Reza Merhof, Dorit Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Few-shot semantic segmentation (FSS) offers immense potential in the field of medical image analysis, enabling accurate object segmentation with limited training data. However, existing FSS techniques heavily rely on annotated semantic classes, rendering them unsuitable for medical images due to the scarcity of annotations. To address this challenge, multiple contributions are proposed: First, inspired by spectral decomposition methods, the problem of image decomposition is reframed as a graph partitioning task. The eigenvectors of the Laplacian matrix, derived from the feature affinity matrix of self-supervised networks, are analyzed to estimate the distribution of the objects of interest from the support images. Secondly, we propose a novel self-supervised FSS framework that does not rely on any annotation. Instead, it adaptively estimates the query mask by leveraging the eigenvectors obtained from the support images. This approach eliminates the need for manual annotation, making it particularly suitable for medical images with limited annotated data. Thirdly, to further enhance the decoding of the query image based on the information provided by the support image, we introduce a multi-scale large kernel attention module. By selectively emphasizing relevant features and details, this module improves the segmentation process and contributes to better object delineation. Evaluations on both natural and medical image datasets demonstrate the efficiency and effectiveness of our method. Moreover, the proposed approach is characterized by its generality and model-agnostic nature, allowing for seamless integration with various deep architectures. The code is publicly available at GitHub. © 2023, CC BY.

关键词： Eigenvalues and eigenfunctions

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The Influence of Human Factors in Ensuring Cybersecurity in Organizations: Effectiveness of PDPA and GDPR Implementation

The Influence of Human Factors in Ensuring Cybersecurity in ...

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Delhi Section Conference (DELCON), IEEE

作者： Ahmad Zia Sharifi Md. Abul Ala Walid Mohammad Dawood Momand Ihsan Ul Haq Safi Hamidullah Khanzai Safi Sadia Hossain Department of Networking and Communication Faculty of Computer Science Nangarhar University Afghanistan Department of Data Science and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur Bangladesh Lautsprecher Teufel GMbH Berlin Germany Department of Internet of Things and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur Bangladesh

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Modern organizations face rising levels of cyber risks, making cybersecurity a top priority for safeguarding sensitive data and maintaining operational continuity. Recent studies find human factors as the most critical contributors to cyber risks. These human-related risks include user misuse, user mistakes, and user malice. A large proportion of cyber risks arise when hackers use social engineering or phishing techniques to manipulate employees into disclosing confidential information. While some employees knowingly engage in these actions, some incidents occur accidentally due to a lack of awareness about cybersecurity protocols. As a result, governments around the world are enacting data protection legislation aimed at enhancing employees' cyber risk. This study explores the perception of US employees on the effectiveness of data protection legislation in countering cyber threats. It surveyed 30 randomly selected employees from various industries. The results showed that employees perceive data protection legislation as effective in mitigating employeerelated cyber risks.

关键词： Industries Protocols computer hacking Phishing Legislation data protection Human factors computer crime Faces

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DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation

arXiv

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arXiv 2023年

作者： Bozorgpour, Afshin Sadegheih, Yousef Kazerouni, Amirhossein Azad, Reza Merhof, Dorit Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Regensburg Germany School of Electrical Engineering Iran University of Science and Technology Iran Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Skin lesion segmentation plays a critical role in the early detection and accurate diagnosis of dermatological conditions. Denoising Diffusion Probabilistic Models (DDPMs) have recently gained attention for their exceptional image-generation capabilities. Building on these advancements, we propose DermoSegDiff, a novel framework for skin lesion segmentation that incorporates boundary information during the learning process. Our approach introduces a novel loss function that prioritizes the boundaries during training, gradually reducing the significance of other regions. We also introduce a novel U-Net-based denoising network that proficiently integrates noise and semantic information inside the network. Experimental results on multiple skin segmentation datasets demonstrate the superiority of DermoSegDiff over existing CNN, transformer, and diffusion-based approaches, showcasing its effectiveness and generalization in various scenarios. The implementation is publicly accessible on GitHub. © 2023, CC BY.

关键词： Diffusion

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IoT data analytic algorithms on edge-cloud infrastructure:A review

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Digital Communications and Networks 2023年第6期9卷 1486-1515页

作者： Abel E.Edje M.S.Abd Latiff Weng Howe Chan School of Computing Faculty of EngineeringUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia Department of Computer Science Delta State UniversityAbrakaP.M.B 01 AbrakaDelta StateNigeria UTM Big Data Centre Ibnu Sina Institute of Scientific and Industrial ResearchUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia

The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime ***,it is constrained by limited data storage and processing *** offloads its massive data stream to edge devices and the cloud for adequate storage and *** further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data *** paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load *** review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load *** addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been *** classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.

关键词： Internet of things Cloud platform Edge Analytic algorithms Processes Network communication protocols

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Open Government data (OGD) Framework for Sustainable Development

Open Government Data (OGD) Framework for Sustainable Develop...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Maria Fasli Amani Yousef Owda Tufail Abbasi Majdi Owda Lampros Stergioulas Bhanu Neupane University of Essex School of Computer Science and Electronic Engineering Colchester UK Faculty of Graduate Studies Arab American University Ramallah West Bank Palestine Faculty of IT & Design The Hague University of Applied Sciences The Hague The Netherlands Faculty of Data Science Arab American University Ramallah West Bank Palestine UNESCO Information and ICT & Sciences Paris France

In the last few years, we have seen the production of data via companies, organizations, or even directly by citizens/consumers increase exponentially. In this new era where almost every aspect of human activity entails the production and consumption of data, Governments play a key role in leading the way to opening up data to stimulate innovation, but also to facilitate transparency and accountability. In this paper, we advocate for this critical role that Governments have to play and the need for a concerted effort in providing a guiding framework to support the opening up of Government data for the wider benefit of society, but in particular to support the Sustainable Development Goals (SDGs). The SDGs, a universal call to action to end poverty, safeguard the planet, and promote prosperity for everyone, are inextricably linked to the appropriate use and dissemination of data. By using the potential of accessible government data, we not only advance the SDGs but also create a more fair and sustainable future for all.

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A Trusted Lesion-assessment Network for Interpretable Diagnosis of Coronary Artery Disease in Coronary CT Angiography 39

A Trusted Lesion-assessment Network for Interpretable Diagno...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ma, Xinghua Fang, Xinyan Zou, Mingye Luo, Gongning Wang, Wei Wang, Kuanquan Qiu, Zhaowen Gao, Xin Li, Shuo Faculty of Computing Harbin Institute of Technology Harbin China Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Faculty of Computing Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China Department of Computer and Data Science Case Western Reserve University Cleveland United States Department of Biomedical Engineering Case Western Reserve University Cleveland United States

ISBN: (纸本)157735897X

Coronary Artery Disease (CAD) poses a significant threat to cardiovascular patients worldwide, underscoring the critical importance of automated CAD diagnostic technologies in clinical practice. Previous technologies for lesion assessment in Coronary CT Angiography (CCTA) images have been insufficient in terms of interpretability, resulting in solutions that lack clinical reliability in both network architecture and prediction outcomes, even when diagnoses are accurate. To address the limitation of interpretability, we introduce the Trusted Lesion-Assessment Network (TLA-Net), which provides a clinically reliable solution for multi-view CAD diagnosis: (1) The causality-informed evidence collection constructs a causal graph for the diagnostic process and implements causal interventions, preventing confounders’ interference and enhancing the transparency of the network architecture. (2) The clinically-aligned uncertainty integration hierarchically combines Dirichlet distributions from various views based on clinical priors, offering confidence coefficients for prediction outcomes that align with physicians’ image analysis procedures. Experimental results on a dataset of 2,618 lesions demonstrate that TLA-Net, supported by its interpretable methodological design, exhibits superior performance with outstanding generalization, domain adaptability, and robustness. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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A Review of Algorithms, datasets, and Criteria in Word Sense Disambiguation with a View to its Use in Islamic Texts 8

A Review of Algorithms, Datasets, and Criteria in Word Sense...

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8th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2020

作者： Farahani, Yasamin Vasheghani Janfada, Behrooz Bidgoli, Behrooz Minaei University of Science and Technology Data Mining Lab Faculty of Computer Engineering Tehran Iran

ISBN: (数字)9781728173016

ISBN: (纸本)9781728173016

Most of the words in a natural language are polysemous;it means, they have multiple senses. Word sense disambiguation is the process of determining the exact sense of the polysemous word by analyzing the context in which the word occurs. Word sense disambiguation is one of the fundamental tasks in the field of natural language processing. However, There have been few studies in word sense disambiguation in Persian and Arabic languages. For the above reason and considering the abundance of ambiguous words and phrases in Islamic texts, the need for a special algorithm to help the disambiguation of such texts is felt. The present study attempted to have an overall review of word sense disambiguation approaches, strategies and issues, in order to open the way for future work on the presentation of similar algorithms for Islamic texts. We review approaches such as semi-supervised, unsupervised, supervised and knowledge-based approaches. The best strategy will be introduced, and the state-of-the-art systems related to it will be compared. © 2020 IEEE.

关键词： Knowledge based systems

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The Effect of Mechanical Stress on the Sensitivity of Fiberoptic Polarization Temperature Sensor

The Effect of Mechanical Stress on the Sensitivity of Fibero...

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New Trends in Signal Processing (NTSP)

作者： Zdenek Vylezich Martin Kyselak Karel Slavicek David Grenar Zuzana Chalk Department of Electrical Engineerings Faculty of Military Technlogy Brno Czech Republic Institute of Computer Science Data and Technical Networks Brno Czech Republic Department of Telecommunication Faculty of Electrical Engineering and Communication Brno Czech Republic Department of Communication Technology Electronic Warfare and Radiolocation Faculty of Military Technology Brno Czech Republic

ISBN: (数字)9788080406370

ISBN: (纸本)9798350362688

The article analyses enhancing the sensitivity of fiber-optic polarization sensors to temperature changes via additional longitudinal strain. This multifunctional sensor is suitable for application as a sensing element for security purposes and has many possible biomedical applications. The testing method consists of stretching the fiber on a proof tester where it is possible to get an exact setting of the operating force, and then applying a container of water at a different temperature. Changes were captured by a laser power meter and plotted into graphs describing several sets of measurements to keep independence on manufacturing. The sensor sensitivity was increased and the proposed solution appeared to be suitable for in-position sensors with minimum additional outlays.

关键词： Temperature sensors Temperature measurement Optical fiber polarization Mechanical sensors Optical fiber sensors Sensitivity Force Sensor phenomena and characterization Optical fiber testing Stress

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MLCardiovascularPrediction: Machine Learning-Based Cardiovascular Disease Prediction

MLCardiovascularPrediction: Machine Learning-Based Cardiovas...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-elghany Zeid Ibrahim Abd Elatif Gomaa Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Like most diseases that affect a human's life, car-diovascular disease is dangerous and unwanted for anyone who has heard of it as it brings a high risk of death. In today's world, most deaths are caused by heart disease as it could come from age, heritage, smoking, and many factors that could seem normal, and it doesn't matter what part of the world you are in. There must be a way to know or predict if this person is at high risk of cardiovascular disease. This paper and project talk about six algorithms: K-Nearest-Neighbour (KNN), Decision Tree (DT), Naive Bayes, Neural Network, Logistic Regression, and Support Vector Machine (SVM). The results came between 60% to 70% in one dataset and from 80 % to 90 % in the second. Still, they mostly favor the Neural Network algorithm as it can learn and model around complex relationships. The SVM showed different outputs between the two datasets as one is significantly larger than the other, possibly due to the outliers in both datasets being different and more complex in the larger one. Cardiovascular disease can be very dangerous, but finding and discovering it soon can be a lifesaver. These algorithms also can help find and predict it, as it was shown with not just decent, but good and respectable success rates for each one.

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