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Proceedings of the Winter Simulation Conference

作者： Maciej K. Wozniak Luke Liang Hieu Phan Philippe J. Giabbanelli Department of Intelligent Systems KTH Royal Institute of Technology Stockholm SWEDEN Department of Computer Science & Software Engineering Miami University Oxford OH USA

After designing a simulation and running it locally on a small network instance, the implementation can be scaled-up via parallel and distributed computing (e.g., a cluster) to cope with massive networks. However, implementation changes can create errors (e.g., parallelism errors), which are difficult to identify since the aggregate behavior of an incorrect implementation of a stochastic network simulation can fall within the distributions expected from correct implementations. In this paper, we propose the first approach that applies machine learning to traces of network simulations to detect errors. Our technique transforms simulation traces into images by reordering the network's adjacency matrix, and then training supervised machine learning models. Our evaluation on three simulation models shows that we can easily detect previously encountered types of errors and even confidently detect new errors. This work opens up numerous opportunities by examining other simulation models, representations (i.e., matrix reordering algorithms), or machine learning techniques.

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Owl Ontology for Liver Diseases Using Protégé & WEB-V-OWL

Owl Ontology for Liver Diseases Using Protégé & WEB-V-OWL

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Zaeem Anwaar Farasat Ullah Khan Mehwish Naz Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan

ISBN: (纸本)9781665464789

Science and health care systems are working hand in hand to cater and support each other in the current era. The liver is one of the important body parts that need to work appropriately for a human body. But sometimes the most hazardous reasons for liver problems are infections or diseases like Hepatitis because they remain for an extended time and direct to dangerous difficulties (liver swelling, liver cancer, etc.). Liver hepatitis contains a diversity of different types of viruses: hepatitis A, B, C, and D. This study presents the design and implementation of liver hepatitis ontology. Protege-Owl and WEB-V-OWL is used for implementing and its visualization. The proposed ontology is portable and can be edited for further addition of concepts in the future. Web ontology language is used to implement this proposed ontology. By developing the Hepatitis ontology, both Intelligent health care systems and physicians can share, reason, and exploit this knowledge in different ways.

关键词： Visualization Liver cancer Liver diseases OWL Medical services Ontologies Diseases

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Performance Comparison of Word2Vec Models for Detecting Arabic Hate Speech on Social Networks

Performance Comparison of Word2Vec Models for Detecting Arab...

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2022 International Conference on Emerging Trends in Computing and engineering Applications, ETCEA 2022

作者： Al-Saqqa, Samar Awajan, Arafat Hammo, Bassam Princess Sumaya University for Technology Department of Computer Science Amman Jordan The University of Jordan King Abdullah II School for IT Department of Information Technology Amman Jordan Mutha University Department of Computer Science Amman Jordan Princess Sumaya University for Technology Department of Software Engineering Amman Jordan The University of Jordan King Abdullah II School for IT Department of CIS Amman Jordan

ISBN: (纸本)9781665477093

Detecting hate speech has become increasingly important for online communities. Despite emerging research to address the problem, more efforts are still needed to improve the performance of detection methods for the Arabic language. In this paper, we compare the performance of two Word2Vec models;the continuous bag of words (CBoW) and skip-gram using a dataset collected from multiple social media platforms;Facebook, Twitter, YouTube, and Instagram. We compared seven machine learning (ML) algorithms and measured their performance using different evaluation metrics. The CBoW model outperformed the skip-gram model in terms of accuracy. In addition, using a higher embedding dimensional value resulted in a better performance. © 2022 IEEE.

关键词： Machine learning

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Verilua: An Open Source Versatile Framework for Efficient Hardware Verification and Analysis Using LuaJIT

Verilua: An Open Source Versatile Framework for Efficient Ha...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Ye Cai Chuyu Zheng Wei He Dan Tang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Beijing Institute of Open Source Chip Beijing China Institute of Computing Technology Chinese Academy of Sciences (lCT) Beijing China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The growing complexity of hardware verification highlights limitations in existing frameworks, particularly regarding flexibility and reusability. Current methodologies often require multiple specialized environments for functional verification, waveform analysis, and simulation, leading to toolchain fragmentation and inefficient code reuse. This paper presents Verilua, a unified framework leveraging LuaJIT and the Verilog Procedural Interface (VPI), which integrates three core functional-ities: Lua-based functional verification, a scripting engine for RTL simulation, and waveform analysis. By enabling complete code reuse through a unified Lua codebase, the framework achieves a 12x speedup in RTL simulation compared to cocotb and a 70x improvement in waveform analysis over state-of-the-art solutions. Through consolidating verification tasks into a single platform, Verilua enhances efficiency while reducing tool fragmentation and learning overhead, addressing critical challenges in modern hardware design.

关键词： Codes Europe Hardware Complexity theory Signal analysis Hardware design languages Engines

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Investigating Vision Transformer Models for Low-Resolution Medical Image Recognition 18

Investigating Vision Transformer Models for Low-Resolution M...

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18th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2021

作者： Adjei-Mensah, Isaac Zhang, Xiaoling Baffour, Adu Asare Agyemang, Isaac Osei Yussif, Sophyani Banaamwini Agbley, Bless Lord Y. Sey, Collins University of Electronic Science and Technology of China School of Information and Communication Engineering China University of Electronic Science and Technology of China School of Information and Software Engineering China University of Electronic Science and Technology of China School of Computer Science and Engineering China

ISBN: (纸本)9781665413640

Vision Transformers use self-attention techniques to learn long-range spatial relations to focus on the relevant parts of an image. They have achieved state-of-the-art results in many computer vision tasks. Recently, some methods have to leverage Vision Transformer-based models to tackle tasks in medical imaging. However, Vision Transformer emphasizes the low-resolution features due to the repetitive downsamplings, which result in a loss or lack of detailed localization information, making it highly unfit for low-level image recognition. In this paper, we investigate the performance of Vision Transformer on low-level medical images and contrast it with convolutional neural networks. The experimental results show that Convolutional Neural Network outperforms the Vision Transformer-based models on all four datasets. © 2021 IEEE.

关键词： Convolutional neural networks

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A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Confirmed Cases Prediction based on Vaccination

A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Con...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Marzia Ahmed Ahmad Johari Mohamad Mostafijur Rahman Mohd Herwan Sulaiman Mohammod Abul Kashem Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang (UMP) Pahang Malaysia Department of Software Engineering Daffodil International university Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Department of Computer Science and Engineering DUET Gazipur Bangladesh

Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics.

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Incentive-driven Federated Learning in Mobile Edge Networks

Incentive-driven Federated Learning in Mobile Edge Networks

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International Conference on Distributed Computing Systems Workshop

作者： Yanlang Zheng Huan Zhou Liang Zhao Shouzhi Xu Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Federated Learning (FL) is proposed as a privacy-preserving distributed learning methodology that can better protect the privacy and reduce communication costs. To stimulate sufficient User Equipments (UEs) to participate in FL, proper incentives need to be designed for FL. Existing incentive mechanisms do not jointly consider UE selection and local learning accuracy optimization to reduce the training expenditure. This paper designs a reverse auction-based incentive mechanism for FL to minimize the training expenditure of Base Station (BS). To this end, we first propose a Greedy Winner Determination (GWD) algorithm to select UEs with the minimum bidding prices. Then, we incorporate the Particle Swarm Optimization (PSO)-based local learning accuracy optimization into UE selection to further reduce the training expenditure of BS. In addition, we design a Vickrey Clarke Groves (VCG)-based payment rule to determine the payment to each participating UE. The simulation experiments show that our proposed PSO with Winner Determination (PSOWD) algorithm is superior to other existing methods in different scenarios.

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An Integrated Approach to Enhance Legal Information Retrieval of Sri Lankan Supreme Court Verdicts

An Integrated Approach to Enhance Legal Information Retrieva...

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Advancements in Computing (ICAC), International Conference on

作者： Asel Jayasooriya Akeel Ahamed Yomal Bandara Chamod Gavindya Dharshana Kasthurirathna Lakmini Abeywardhana Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept.of Computer Science & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Legal professionals and law students often grapple with the arduous task of locating prior Supreme Court rulings for argument preparation and academic study, leading to a laborious and resource-intensive process. This study introduces an innovative approach to streamline the retrieval of legal information from Sri Lankan Supreme Court verdicts. The method centers on developing a Custom Named Entity Recognition (NER) model, boasting a remarkable accuracy exceeding 90%. This model efficiently extracts crucial legal entities from Supreme Court rulings, readily available on the Ministry of Justice's Supreme Court website. The high accuracy ensures precise entity extraction, followed by systematic organization within a dedicated database. Subsequently, a knowledge graph is formed by linking recorded legal entities, reducing information retrieval time to a mere 179 milliseconds, significantly outperforming existing methods. Moreover, a BART summarization model is crafted to generate concise, accurate, and insightful summaries of Supreme Court decisions, boasting an impressive ROGUE1 score of 85%. This approach revolutionizes legal information retrieval, delivering a user-friendly platform that enhances the identification of cases and fosters a deeper understanding, ultimately elevating the quality of legal research and practice.

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Decentralized Voltage Regulation and Accurate Current Sharing in a Parallel Buck Converters With Optimized Piecewise Linear Droop Control

Decentralized Voltage Regulation and Accurate Current Sharin...

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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Aphrodis Nduwamungu Tek Tjing Lie Ioannis Lestas Nirmal-Kumar C Nair Kosala Gunawardane Dept of Elect & Elec Engineering Auckland Univ. of Technology New Zealand Dept of Information Engineering Univ. of Cambridge United Kingdom Dept of Elect Computer & Software Engineering University of Auckland New Zealand School of Elect and Data Eng. Univ of Technology Sydney Australia

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

For DC microgrids (MGs) with load variations and fixed operating points, the use of both linear and nonlinear droop controllers and their extensions is recommended. However, in future architectures with diverse loads ranging from light to heavy, including constant power loads, and fluctuating system parameters, this approach becomes impractical. Linear droop control, with a single droop coefficient, faces challenges in ensuring equitable current sharing due to cable resistance-induced voltage drops. Nonlinear droop control, with a high droop coefficient at heavy loads, may lead to suboptimal current sharing during light load conditions. To address these issues, an optimized piecewise linear droop control is proposed as an intermediary solution, demonstrating superior voltage regulation and current sharing in simulations, particularly under varying loads. Worst-case scenarios highlight the controller's dynamic adjustment of the droop coefficient for each load segment. Stability assessments of parallel buck converters have been conducted by varying critical parameters.

关键词： Buck converters Accuracy Power system dynamics Power system stability Load management Probabilistic logic Stability analysis

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Fingerprint Matching for Noisy and Distorted Patterns Using a Siamese Network With ResNet50 and Multihead Attention

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IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Kumar Lehana, Parveen Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii Ali Alzubi, Ahmad University of Jammu DSP Laboratory Department of Electronics Jammu180006 India University of Jammu Department of Computer Science and IT Jammu180006 India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab Rajpura140401 India Manipal University Jaipur Department of Computer Science & Communication Engineering Jaipur303007 India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa42-200 Poland Faculty of Informatics and Software Engineering National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv03056 Ukraine King Saud University Community College Department of Computer Science Riyadh11451 Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

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