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Quantitative Assessment of Generative Large Language Models on Design Pattern Application

computers, Materials & Continua 2025年第3期82卷 3843-3872页

作者： Dae-Kyoo Kim Department of Computer Science and Engineering Oakland University115 Library Dr.RochesterMI 48309USA

Design patterns offer reusable solutions for common software issues,enhancing *** advent of generative large language models(LLMs)marks progress in software development,but their efficacy in applying design patterns is not fully *** recent introduction of generative large language models(LLMs)like ChatGPT and CoPilot has demonstrated significant promise in software *** assist with a variety of tasks including code generation,modeling,bug fixing,and testing,leading to enhanced efficiency and *** initial uses of these LLMs have had a positive effect on software development,their potential influence on the application of design patterns remains *** study introduces a method to quantify LLMs’ability to implement design patterns,using Role-Based Metamodeling Language(RBML)for a rigorous specification of the pattern’s problem,solution,and transformation *** method evaluates the pattern applicability of a software application using the pattern’s problem *** deemed applicable,the application is input to the LLM for pattern *** resulting application is assessed for conformance to the pattern’s solution specification and for completeness against the pattern’s transformation *** the method with ChatGPT 4 across three applications reveals ChatGPT’s high proficiency,achieving averages of 98%in conformance and 87%in completeness,thereby demonstrating the effectiveness of the *** RBML,this study confirms that LLMs,specifically ChatGPT 4,have great potential in effective and efficient application of design patterns with high conformance and *** opens avenues for further integrating LLMs into complex software engineering processes.

关键词： Design patterns large language models pattern application pattern-based refactoring quantitative assessment

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All in One Place: Ensuring Usable Access to Online Shopping Items for Blind Users

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Proceedings of the ACM on Human-computer Interaction 2024年第EICS期8卷 1-25页

作者： Prakash, Yash Nayak, Akshay Kolgar Sunkara, Mohan Jayarathna, Sampath Lee, Hae-Na Ashok, Vikas Old Dominion University Department of Computer Science NorfolkVA United States Michigan State University Department of Computer Science and Engineering East LansingMI United States

Perusing web data items such as shopping products is a core online user activity. To prevent information overload, the content associated with data items is typically dispersed across multiple webpage sections over multiple web pages. However, such content distribution manifests an unintended side effect of significantly increasing the interaction burden for blind users, since navigating to-and-fro between different sections in different pages is tedious and cumbersome with their screen readers. While existing works have proposed methods for the context of a single webpage, solutions enabling usable access to content distributed across multiple webpages are few and far between. In this paper, we present InstaFetch, a browser extension that dynamically generates an alternative screen reader-friendly user interface in real-time, which blind users can leverage to almost instantly access different item-related information such as description, full specification, and user reviews, all in one place, without having to tediously navigate to different sections in different webpages. Moreover, InstaFetch also supports natural language queries about any item, a feature blind users can exploit to quickly obtain desired information, thereby avoiding manually trudging through reams of text. In a study with 14 blind users, we observed that the participants needed significantly lesser time to peruse data items with InstaFetch, than with a state-of-the-art solution. © 2024 Owner/Author.

关键词： User interfaces

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Polarization-encodable photonic memory cells using nextgeneration 2D phase-change materials

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Nano Research 2025年第3期18卷 510-518页

作者： Amin Shafiee Linhong Chen Mahdi Nikdast Jie Yao Department of Electrical and Computer Engineering Colorado State UniversityFort CollinsCO 80523USA Department of Materials Science and Engineering University of CaliforniaBerkeleyCA 94720USA

Integration of phase-change materials(PCMs)created a unique opportunity to implement reconfigurable photonics devices that their performance can be tuned depending on the target *** PCMs such as Ge-Sb-Te(GST)and Ge-Sb-Se-Te(GSST)rely on melt-quench and high temperature annealing processes to change the organization of the molecules in the materials’*** a reorganization leads to different optical,electrical,and thermal properties which can be exploited to implement photonic memory cells that are able to store the data at different resistance or optical transmission *** the great promise of conventional PCMs for realizing reconfigurable photonic memories,their slow and extremely power-hungry thermal mechanisms make scaling the systems based on such devices *** addition,such materials do not offer a stable multi-level response over a long period of *** address these shortcomings,the research carried out in this study shows the proof of concept to implement next-generation photonic memory cells based on two-dimensional(2D)birefringence PCMs such as SnSe,which offer anisotropic optical properties that can be switched *** demonstrate that by leveraging the ultrafast and low-power crystallographic direction change of the material,the optical polarization state of the input optical signal can be *** enables the implementation of next-generation high-speed polarization-encodable photonic memory cells for future photonic computing *** to the conventional PCMs,the proposed SnSe-based photonic memory cells offer an ultrafast switching and low-loss optical response relying on ferroelectric property of SnSe to encode the data on the polarization state of the input optical *** a polarization encoding scheme also reduces memory read-out errors and alleviates the scalability limitations due to the optical insertion loss often seen in optical transmission encoding.

关键词： ferroelectric materials phase-change materials photonic memory optical storage cells polarization encodable photonic memories optical polarization

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Advances in Manta Ray Foraging Optimization:A Comprehensive Survey

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Journal of Bionic engineering 2024年第2期21卷 953-990页

作者： Farhad Soleimanian Gharehchopogh Shafi Ghafouri Mohammad Namazi Bahman Arasteh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmiaIran Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabrizIran Department of Computer Engineering Maybod BranchIslamic Azad UniversityMaybodIran Department of Software Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic *** in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault *** biologically inspired strategies allow for effective solutions to intricate physical *** its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization *** utility and benefits have found traction in numerous academic *** its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference *** paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization *** trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.

关键词： Manta ray foraging optimization Metaheuristic algorithms Hybridization Improved Optimization

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A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

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computers, Materials & Continua 2024年第12期81卷 3903-3926页

作者： Umapathi Krishnamoorthy Shanmugam Jagan Mohammed Zakariah Abdulaziz S.Almazyad K.Gurunathan Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Department of Computer Sciences and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Mount Zion College of Engineering and TechnologyPudukkottai622507India

Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to normal brain signals and thus can be identified from EEG *** the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure *** effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between *** identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert *** granularity is essential for improving patient-specific interventions and developing proactive seizure management *** study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure *** enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and ***,k-fold cross-validation ensures the model’s reliability and generalizability across different data *** and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG *** summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinic

关键词： Bonn EEG dataset cross-validation genetic algorithm batch normalization seizure classification stochastic gradient

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Revolutionizing Influenza A Detection in Humans: Unleashing the Power of XGBoost Classification 2

Revolutionizing Influenza A Detection in Humans: Unleashing ...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Nikhil, V. Sahaaya Arul Mary, S.A. Natarajan, B. Bajulunisha, A. Anuradha, R.S. Venkatraman, K. Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VIT University School of Computer Science Engineering Chennai India M.A.M College of Engineering and Technology Department of Artificial Intelligence & Data Science Tiruchirappalli621105 India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350383287

Influenza A, a zoonotic virus potentially affecting and infecting humans, poses a significant global health threat. This research paper presents a comprehensive study on predicting Influenza A outbreaks by applying the XGBoost classification algorithm. The study utilizes two substantial datasets: one from the World Health Organization (WHO) containing global Influenza A epidemiological data from 2003 to 2023 and the other encompassing Influenza A infections in New York counties from 2009 to 2019. Both datasets underwent pre-processing, with essential features selected to develop an XGBoost Classification model for forecasting Influenza A cases in humans. The models exhibited impressive accuracy and performance metrics. Specifically, the WHO dataset demonstrated an AUC of 0.996 and an accuracy score of 0.99, while the New York dataset displayed an AUC of 0.992 and an accuracy score of 0.978. These outcomes underscore the model's capability to differentiate between positive and negative cases, showcasing high precision, accuracy, and F1 scores. The implications of this research extend to various domains, including the early detection of outbreaks, informed decision-making processes, and the implementation of public health interventions. The findings emphasize the pivotal role of high-quality and comprehensive datasets in training effective predictive models. Furthermore, the study points towards potential future avenues, such as personalized predictions, geo-spatial analysis, and integrating these predictive models with healthcare systems. © 2024 IEEE.

关键词： Classification (of information)

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A Voting Approach for Heart Sounds Classification Using Discrete Wavelet Transform and CNN Architecture

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SN computer science 2024年第2期5卷 251页

作者： Das, Sunanda Ahsan, Sk. Md. Masudul Rahman, Mahfujur Karim, Md. Shahriar Department of Computer Science and Engineering Khulna University of Engineering & amp

Cardiovascular diseases (CVDs) are a group of diseases that affect the heart or blood vessels and are the leading cause of mortality around the world. The main focus of this work is to classify heart sounds accurately before the condition of the heart worsens. Over the past few decades, audio-based classifications of medical data have received high consideration among various researchers. However, there are mainly two drawbacks while working with audio data. (1) variable length of the audio data and, (2) an inadequate number of class quintessential audio samples. In this work, reflection operation and the sliding window approach are employed to generate fixed-sized audio data. The proposed method applied Discrete Wavelet Transform (DWT) and Mel-Spectrogram for feature extraction. Furthermore, different augmentation techniques such as time-stretching and pitch-shifting are utilized in the method so that the proposed deep learning-based CNN architecture can be trained on a large amount of data. The proposed method is verified using two datasets provided by the ‘PASCAL Heart Sounds Challenge’, each of which contains a small number of heart sound samples of various lengths. In comparison, the experimental outcomes exhibit that the proposed approach outperforms many state-of-the-art methods with respect to sensitivity, specificity, precision, Youden index, etc. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Augmentation Convolutional neural network (CNN) Discrete wavelet transform (DWT) Sliding window Spectrogram

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DLT-Based Cloud File Sharing System 6

DLT-Based Cloud File Sharing System

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Chouhan, Ruchika Thatipamula, Ajay Thumati, M.V.S. Rama Krishna Sharma, Sachin Tandra, Vennela Tadiboina, Ramanjaneyulu Department Computer Science Engineering Parul University Vadodara India Department Computer Science Engineering Parul University Hyderabad India Department Computer Science Engineering Parul University Vinukonda India Department Computer Science Engineering Parul University Indore India Department Computer Science Engineering Parul University Vijayawada India

ISBN: (纸本)9798350369748

Cloud storage is crucial for managing large datasets, but dependence on a single cloud space raises security concerns. Conversely, Distributed Ledger Technology (DLT) provides a secure cloud-based storage system operating on a peer-to-peer network. In our proposed system, user files are encrypted and distributed across multiple peers using the Interplanetary File System (IPFS) protocol, with hash values stored in the *** project integrates DLT and a cloud platform for storing filesfrom various users. Our project, Distributed Cloud File Sharing Technology (DCFST), merges distributed computing principles with cloud technology to overcome traditional file-sharing limitations. It enables secure file sharing, storage, and access across interconnected devices, ensuring data integrity and authentication through blockchain. Smart contracts automate access control,granting precise content control to users. DCFST orchestrates a peer-to-peer network of nodes, with a central coordinator managing node discovery, file indexing, and load balancing. The implementation utilizes Python, JavaScript, and Ethereum for smart contract execution. DCFST introduces a resilient, user-centric file-sharing ecosystem by merging distributed systems with cloud infrastructure. It contributes to decentralized technology evolution, revolutionizing data collaboration and access paradigms. © 2024 IEEE.

关键词： Distributed ledger

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The use of multi-task learning in cybersecurity applications: a systematic literature review

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Neural Computing and Applications 2024年第35期36卷 22053-22079页

作者： Ibrahim, Shimaa Catal, Cagatay Kacem, Thabet Department of Computer Science and Engineering Qatar University 2713 Doha Qatar Department of Computer Science and Information Technology University of the District of Columbia WashingtonDC20008 United States

Cybersecurity is crucial in today’s interconnected world, as digital technologies are increasingly used in various sectors. The risk of cyberattacks targeting financial, military, and political systems has increased due to the wide use of technology. Cybersecurity has become vital in information technology, with data protection being a major priority. Despite government and corporate efforts, cybersecurity remains a significant concern. The application of multi-task learning (MTL) in cybersecurity is a promising solution, allowing security systems to simultaneously address various tasks and adapt in real-time to emerging threats. While researchers have applied MTL techniques for different purposes, a systematic overview of the state-of-the-art on the role of MTL in cybersecurity is lacking. Therefore, we carried out a systematic literature review (SLR) on the use of MTL in cybersecurity applications and explored its potential applications and effectiveness in developing security measures. Five critical applications, such as network intrusion detection and malware detection, were identified, and several tasks used in these applications were observed. Most of the studies used supervised learning algorithms, and there were very limited studies that focused on other types of machine learning. This paper outlines various models utilized in the context of multi-task learning within cybersecurity and presents several challenges in this field. © The Author(s) 2024.

关键词： Cybersecurity

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A feature-enhanced multiscale attention approach for automated hand bone segmentation

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Multimedia Tools and Applications 2025年第16期84卷 15949-15969页

作者： Nagaraju, Y. Venkatesh Thanu Shree Yadav, P.R. Vaishnavi, A. Tejashree, S.V. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Karnataka Bengaluru560001 India

In the medical field, comprehensive analysis of bone structures is paramount for assessing skeletal health and diagnosing conditions. X-ray imaging serves as a cornerstone in bone age evaluation and the fabrication of implants. In addition to clinical applications, automated bone structure analysis aids students in studying anatomy and radiology by providing accurate and consistent representations. This automation has the potential to significantly reduce processing time and enhance students’ understanding of bone structures and their variations, thereby improving their understanding and training in the medical field. This study presents the Adaptive Multiscale Expansion Segmentation Model (AMESM), a lightweight encoder-decoder-based multiclass semantic segmentation model for segmenting hand bones. The proposed method assists physicians in tasks such as growth analysis, hand movement assessment, and implant design. The model incorporates a Multi-Scale Attention Block that improves feature representation and segmentation accuracy. The experimental results demonstrate promising performance, with a Mean Intersection over Union (mIoU) of 88.47% and a Dice Score of 93.86%. The comprehensive analysis confirms the model’s ability to achieve accurate segmentation while remaining computationally efficient. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Automation

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