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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Tehsin, Sara Hassan, Ali Riaz, Farhan Nust Department of Computer and Software Engineering Islamabad Pakistan Hitec University Deparment of Computer Engineering Taxila Pakistan

ISBN: (纸本)9798350394764

Signature verification plays an important role in document authentication. The efficient and robust system is required for forgery detection in documents in the presence of distortion such as rotation, scaling and noise. This paper proposes a writer-dependent signature verification method, where hand-crafted features are fused with one-dimensional convolutional neural network features to cater different variance in the whole verification process. Proposed feature extraction methodology is validated by bagged ensemble learning technique in which machine learning classifier's decision is finalized based on majority voting. The effectiveness of the proposed signature verification approach has been evaluated across three distinct publicly accessible datasets. The outcomes highlight the versatility of this method. Moreover, the proposed system surpasses the performance of other pre-existing systems documented in the literature. © 2024 IEEE.

关键词： Learning systems

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Generative artificial intelligence for distributed learning to enhance smart grid communication

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International Journal of Intelligent Networks 2024年第1期5卷 267-274页

作者： Sajjadi Mohammadabadi, Seyed Mahmoud Entezami, Mahmoudreza Karimi Moghaddam, Aidin Orangian, Mansour Nejadshamsi, Shayan University of Nevada Computer Science and Engineering Department RenoNV United States Concordia University Computer Science and Software Engineering Department MontrealQC Canada University of Zanjan Department of Mathematics and Computer Science Zanjan Iran Islamic Azad University South Tehran Branch Department of Engineering Tehran Iran MontrealQC Canada

Machine learning models are the backbone of smart grid optimization, but their effectiveness hinges on access to vast amounts of training data. However, smart grids face critical communication bottlenecks due to the ever-increasing volume of data from distributed sensors. This paper introduces a novel approach leveraging Generative Artificial Intelligence (GenAI), specifically a type of pre-trained Foundation Model (FM) architecture suitable for time series data due to its efficiency and privacy-preserving properties. These GenAI models are distributed to agents, or data holders, empowering them to fine-tune the foundation model with their local datasets. By fine-tuning the foundation model, the updated model can produce synthetic data that mirrors real-world grid conditions. The server aggregates fine-tuned model from all agents and then generates synthetic data which considers all data collected in the grid. This synthetic data can be used to train global machine learning models for specific tasks like anomaly detection and energy optimization. Then, the trained task models are distributed to agents in the grid to leverage them. The paper highlights the advantages of GenAI for smart grid communication, including reduced communication burden, enhanced privacy through anonymized data transmission, and improved efficiency and scalability. By enabling a distributed and intelligent communication architecture, GenAI introduces a novel way for a more secure, efficient, and sustainable energy future. © 2024 The Authors

关键词： Smart power grids

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Work-in-Progress: ESOps - An Agile Pipeline for Next-Generation Embedded Systems Development 24

Work-in-Progress: ESOps - An Agile Pipeline for Next-Generat...

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24th ACM SIGBED International Conference on Embedded software, EMSOFT 2024

作者： Al Maruf, Md Azim, Akramul Ontario Tech University Department of Electrical Computer and Software Engineering ON Canada

ISBN: (纸本)9798350356410

Embedded Systems Operations (ESOps) introduces a feature-driven development pipeline for next-generation embedded system, leveraging domain-specific Large Language Models (LLMs) to expedite the development process. Targeted at industries such as automotive, aerospace, and healthcare, ESOps overcomes traditional development barriers by enabling a profound understanding of legacy codebase and integrating innovative AI-driven methodologies. This pipeline ensures systems predictability and reliability, meets stringent constraints, and shortens time-to-market by optimizing both the development and operational phases. © 2024 IEEE.

关键词： Artificial intelligence

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Author profiling from Romanized Urdu text using transfer learning models

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Neural Computing and Applications 2025年第6期37卷 4455-4470页

作者： Ali, Abid khan, Muhammad Sohail Khan, Muhammad Amin Khan, Sajid Ullah Khan, Faheem Department of Computer Software Engineering University of Engineering & Technology Mardan KPK Mardan23200 Pakistan Islamabad H-11/4 Islamabad44000 Pakistan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Engineering Gachon University Seongnam-Si Korea Republic of

This research concentrates on author profiling using transfer learning models for classifying age and gender. The investigation encompassed a diverse set of transfer learning techniques, including Roberta, BERT, ALBERT, Distil BERT, Distil Roberta, ELECTRA, and XLNet. Through meticulous evaluation using metrics such as the Matthews Correlation Coefficient, Accuracy, Precision, Recall, and F1 Score, the study examined the efficacy of these models. The curated dataset was divided for gender and age tasks, resulting in robust gender prediction with the XLNet model and age prediction with the BERT model. Notably, the XLNet model achieved the highest MCC (0.7946), Accuracy (0.8957), Precision (0.8992), Recall (0.8957), and F1 Score (0.8958) values in gender classification, while the BERT model excelled in age prediction with an MCC of (0.7338), Accuracy of (0.8220), Precision of (0.8324), Recall of (0.8220), and F1 Score of (0.8243). Visualized outcomes provide valuable insights into the model’s performance nuances, paving the way for their practical implementation. This research offers novel contributions to author profiling tasks, bridging the gap between theory and real-world applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Abstract Interpretation for PLONKish Circuits 34

Abstract Interpretation for PLONKish Circuits

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34th Annual International Conference on Collaborative Advances in software and Computing, CASCON 2024

作者： Soureshjani, Fatemeh Heidari Gorzny, Jan Polytechnique Montréal Computer Engineering and Software Engineering Department Canada Zircuit Canada

ISBN: (纸本)9798331504830

Zero-knowledge proof systems are becoming powerful tools in domains like blockchain networks. In this work, we improve abstract interpretation methods to sanity check PLONKish arithmetizations of computation, such as those used in the Halo2 zero-knowledge proof system. Our work aims to improve the accuracy of checks for unused gates, assigned but unconstrained values, and possibly under-constrained circuits, all of which may indicate a bug in the circuits. We show how copy constraints and arbitrary lookup functions can be used in abstract interpretation based analysis of these circuits. We are motivated to revisit this topic as these circuits, through their use in general purpose blockchain networks, may be responsible for the security of millions of dollars in cryptocurrency. © 2024 IEEE.

关键词： Blockchain

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A Social Distancing Framework Based on GPS and Bluetooth Empowered by Feature-based Machine Learning Algorithm

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 118-127页

作者： Khan, Muhammad Adnan Rehman, Abdur Abbas, Sagheer Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The current COVID-19 epidemic is responsible for causing a catastrophe on a global scale due to its risky spread. The community’s insecurity is growing as a result of a lack of appropriate remedial measures and immunization against the disease. In this case, social distancing is thought to be an effective barrier against the spread of the contagion virus as the risk of virus transmission can be reduced by avoiding direct contact with people. Thus, the goal of this research is to develop and improve an AI (Artificial Intelligence) system architecture for social distance monitoring. The framework could also use the GPS (Global Positioning System) to recognize human separation through cell phones. The transition learning framework is also applied to improve the consistency of the existing system. In this manner, the detection system uses a pre-trained technique that takes a Bluetooth dataset and location-sharing dataset to link to an additional level. In an attempt to approximate social distancing breaches among people, we used Bluetooth technology along with GPS distance estimation and set a threshold. To predict if the distance value exceeds the required social distance standard, a violation threshold is calculated and then it sends an alarm to every individual who is not maintaining social distancing. In response, the individual who breaks the social distance limit is also monitored using a detection approach. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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Ontology Based Test Case Reduction in Regression Testing 26

Ontology Based Test Case Reduction in Regression Testing

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Farooq, Ummer Qamar, Usman Shaukat, Arslan Bakar, Abu Ashraf, Zubia Naz, Mehwish Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350359015

Ontology-based test case reduction in regression testing during software testing is a sophisticated approach that leverages semantic knowledge representation to optimize the selection of test cases. By harnessing domain-specific ontologies, this methodology facilitates a more intelligent and efficient reduction process, ensuring that only the most relevant test cases are retained for subsequent testing cycles. This research paper explores the application of ontology-based technique to enhance the efficiency of regression testing in software development. By leveraging ontological knowledge representations, the study introduces a novel approach for reducing the number of test cases while preserving test coverage, thereby significantly optimizing the regression testing process. The proposed framework not only streamlines testing efforts but also enhances the overall quality assurance of software systems, making it a valuable contribution to software engineering practices. © 2023 IEEE.

关键词： ontology regression testing semantic knowledge software development software testing test case reduction

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Design of high output DC/DC boost circuit without boost transformer 8

Design of high output DC/DC boost circuit without boost tran...

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8th International Conference on Communication and Information Systems, ICCIS 2024

作者： Hong, Qin Zhou, Liang Jia, Zhongning Liu, Chun Liu, Jianbo Software Engineering Institute of Guangzhou Department of Computer Science Guangzhou China

ISBN: (纸本)9798331531171

The development of IOT smart devices has necessitated increasingly stringent requirements for DC power sources in terms of output voltage, size, and cost. For instance, Avalanche Photodiodes (APDs), utilized as photodetectors, require a significantly high reverse bias voltage during the detection of feeble optical signals to achieve optimal gain and high signal-to-noise ratio, thereby ensuring exceptional sensitivity. To address the diverse demands of various smart devices for DC power, a high-output DC/DC boost circuit without a step-up transformer has been innovatively designed. This circuit is comprised of four fundamental components: an oscillator circuit, a comparator circuit, a voltage multiplier circuit, and a constant-current and voltage-stabilizing circuit. The paper conducts a comprehensive theoretical analysis of the circuit's topology, parameter selection, and operational process, which is further validated through Multisim simulation. Additionally, a PCB circuit was designed, fabricated, and underwent rigorous practical testing. The test results conclusively demonstrate that the designed boost circuit proficiently provides APDs with the appropriate bias voltage, eliminating the need for boost transformer. It boasts high output voltage, a simplistic structure, small size, and low cost, enabling it to seamlessly share the same input power source with the APD detection circuit and be integrated into the same PCB layout. © 2024 IEEE.

关键词： avalanche photodiode boost circuit direct current stabilized power supply oscillator voltage multiplier circuit

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A Systematic Literature Review of Latest Cloud Forensic Frameworks, Tools and Challenges 2

A Systematic Literature Review of Latest Cloud Forensic Fram...

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2nd International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2023

作者： Farooq, Ummer Shaukat, Arslan Butt, Wasi Haider Hassan, Ali Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350305654

Background: Cloud computing is widely used in this era of IoTs. Cloud users utilize cloud computing to access various cloud services. The defects in cloud services are exploited by suspicious actors. On the other hand, cloud forensics help against such suspicious actors. It has been observed on many occasions that cloud services were not well designed. Objective: Cloud computing with its services exposed to cyber threats, due to the bad implementation of cloud services, helps suspicious actors to exploit vulnerabilities in the cloud environment. The main objective is to identify different cloud forensic frameworks. Method: We have used the method of systematic literature review to find and analyze different research studies, which mainly are published between 2010-2022. Results: In this paper, we have discovered 36 different cloud forensic frameworks, with some forensic tools. In this SLR we also mentioned some challenges of cloud forensics with recommendations. Conclusion: This systematic literature review unveils the latest developments in cloud forensic frameworks, tools, and challenges. The insights provided in this paper will serve as an invaluable resource for future researchers in this domain, facilitating the enhancement of cloud security practices. © 2023 IEEE.

关键词： Web services

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Fingerprint Fraud Explainability Using Grad-Cam for Forensic Procedures 23

Fingerprint Fraud Explainability Using Grad-Cam for Forensic...

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23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Manai, Elyes Mejri, Mohamed Fattahi, Jaouhar Department of Computer & Software Engineering Faculty of Science and Engineering Laval University Quebec Canada

ISBN: (纸本)9781643685380

This paper investigates the application of GradCAM, an explainable AI (XAI) technique, to enhance the transparency and precision of fingerprint authentication systems in forensics, particularly in detecting fingerprint mutilation-a common method used to evade biometric security measures. Employing the SOCOfing dataset, which contains both unaltered and synthetically altered fingerprint images, we apply GradCAM to visualize and understand the decision-making process of a convolutional neural network (CNN) model trained to recognize and classify these alterations. Our study not only demonstrates the model's effectiveness in identifying different types of fingerprint modifications but also identifies areas where the model's performance can be enhanced. Through detailed visual analysis, we uncover the model's focus points and assess its reliability across various alteration types and difficulty levels. The insights gained underline the potential of XAI in improving the robustness and reliability of biometric verification systems, paving the way for more secure and equitable AI applications in high-stakes environments. © 2024 IOS Press. All rights reserved.

关键词： Convolutional neural networks

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